A Survey on LiDAR-based Autonomous Aerial Vehicles

Fangcheng Zhu; Fanze Kong; Fu Zhang; Haotian Li; Longji Yin; Nan Chen; Rundong Li; Yixi Cai; Yunfan Ren

REVIEW 4 major objections 5 minor 219 references

A Survey on LiDAR-based Autonomous Aerial Vehicles

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This survey argues that LiDAR is the enabling sensor for high-speed, GPS-denied autonomous drone flight, and positions itself as the first dedicated comprehensive review of LiDAR-based autonomous UAV systems.

desk verdict Useful survey with a genuinely good resource table, but its 'first/only' claims and one glaring misattribution mean the historical sections need fixing before I'd trust them. read the letter →

arxiv 2509.10730 v1 pith:FW7HYYKA submitted 2025-09-12 cs.RO

Yunfan Ren , Yixi Cai , Haotian Li , Nan Chen , Fangcheng Zhu , Longji Yin , Fanze Kong , Rundong Li

show 1 more author

Fu Zhang

This is my paper · ORCID

classification cs.RO

keywords LiDAR-basedUAVautonomystateestimationoccupancymappingtrajectoryplanningswarmVTOLtail-sitterGPS-deniednavigation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey sets out to establish that LiDAR is the enabling sensor for autonomous UAV flight in GPS-denied, cluttered environments, and that the field has matured enough to warrant a dedicated review. The authors argue that LiDAR's centimeter-level, long-range 3D measurements support aggressive maneuvers up to 20 m/s and reliable state estimation in poor lighting, unlike cameras. They organize the state of the art into sensing hardware, perception, planning and control, open-source systems, and applications. The paper also claims several milestones: PULSAR as the only self-rotating UAV to achieve fully autonomous navigation, and the first fully autonomous tail-sitter VTOL flight. A sympathetic reader would take the survey as a map of what works and what is missing in LiDAR-based drone autonomy.

What carries the argument

The load-bearing mechanism is the LiDAR sensor itself, in three generations: 2D laser rangefinders, mechanical 3D LiDARs such as the VLP-16, and solid-state LiDARs such as the Livox Avia and MID360. On the software side, the survey identifies LiDAR-inertial odometry (FAST-LIO2 and variants) as the state-estimation workhorse, occupancy grid maps like D-Map for efficient 3D mapping, and safe-flight-corridor trajectory optimization, particularly MINCO-based methods, for high-speed planning. These components together are what the survey claims convert raw point clouds into 20 m/s autonomous flight.

What would settle it

A literature search turning up either an earlier dedicated LiDAR-based UAV survey or an earlier self-rotating UAV with fully autonomous navigation in unknown GPS-denied environments would falsify the paper's central priority claims.

Watch

Extended reading notes

Core claim

The central claim is that LiDAR-based UAV autonomy has progressed from bulky 2D laser mapping on large helicopters to a mature, high-speed capability: modern solid-state LiDARs weighing roughly 265–500 g, combined with LiDAR-inertial odometry and safe-flight-corridor trajectory optimization, enable fully autonomous flight at over 13.7 m/s in cluttered scenes and over 20 m/s in open ones, without GPS. The paper's distinctive assertions are historical and taxonomic: it is the first dedicated comprehensive review of LiDAR-based autonomous UAVs, and within the literature it identifies PULSAR as the only self-rotating UAV to achieve complete autonomous navigation in unknown GPS-denied environment

Load-bearing premise

The survey's value and its 'only' and 'first' claims rest on the assumption that its literature search is complete and its per-paper summaries are accurate; the text itself suggests this assumption is imperfect, since the attribution in Sec. III-A-1 does not match the cited reference.

Editorial extensions

If this is right

If LiDAR is the enabling sensor, further miniaturization and cost reduction (e.g., flash LiDAR, FMCW) will extend high-speed autonomy to smaller and cheaper UAVs.
Two-stage planning—front-end path or corridor search plus back-end trajectory optimization—is the preferred framework for LiDAR-based UAVs, with safe-flight-corridor methods like MINCO becoming standard.
The demonstrated milestones (PULSAR and the autonomous tail-sitter) imply that LiDAR can overcome sensor field-of-view and aerodynamic challenges that camera-based systems have not solved.
LiDAR-based swarms, such as Swarm-LIO2, can achieve cooperative state estimation and tracking in cluttered environments, pointing toward multi-UAV exploration as a near-term application.
Future progress will come from multi-sensor fusion (LiDAR plus camera and radar) and learning-based perception to handle sparsity and adverse weather, rather than from LiDAR alone.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the survey's causal story is right—that sensor range and accuracy drive autonomy speed—then FMCW LiDAR's per-point velocity measurements should trigger the next generation of aggressive-flight algorithms, since they promise motion estimation without scan matching.
The survey's milestone claims are priority claims; I would treat them as hypotheses to be verified by an independent literature search, since the survey's own citation inconsistencies (e.g., the 1998 attribution in Sec. III-A-1) show per-paper summaries can be unreliable.
The survey's framework suggests a testable extension: benchmarking LiDAR-based planners against camera-based ones at equal speed in identical cluttered environments would isolate LiDAR's contribution to autonomy from planning and control.
If the open-source list is representative, the field's center of gravity lies in a few research groups; reproducing these systems outside those groups remains a barrier, so the survey's value may be as much a guide to building as to understanding.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a readable, well-organized survey that people entering LiDAR-based UAV work will find genuinely useful, and the open-source resource table is worth the download on its own. But the priority claims—PULSAR as 'the only one exception' among self-rotating autonomous UAVs, the tail-sitter 'first,' the implicit 'we fill a gap' framing—rest on a literature search that is never documented. There is also a concrete factual error: Sec. III-A-1 attributes a 1998 environmental mapping effort to 'Miller et al.,' but the cited reference [37] is M. Ryan's CMU helicopter paper. That is the kind of mistake that makes you want to check every summary.

What it does well: the planning taxonomy (one-stage vs two-stage; waypoint/distance-field/safe-flight-corridor) is a fair way to organize a messy literature. The sensor evolution story is accurate and well illustrated. The table covers hardware, simulators, calibration, localization, mapping, planning, control, and autopilots. The Web of Science count is simple and reproducible. The authors are major contributors here (FAST-LIO, ROG-Map, PULSAR), and their systems are real and important; self-citation is not a flaw in itself. It is a weakness only when exclusivity claims are not checked.

The soft spots: no search protocol, no inclusion criteria, no description of how the 'comprehensive' corpus was assembled. That matters because the paper makes categorical claims. Maybe PULSAR really is the only fully autonomous self-rotating LiDAR UAV; I don't know, and this paper does not give me a way to verify it. Same for the tail-sitter priority. Either document the search or soften the language. Fix the Miller/Ryan misattribution and spot-check the other historical statements. Minor: 'ourdoor' typo in Sec. VI-B-2.

Who is it for: new graduate students, practitioners looking for open-source stacks, anyone needing a quick map of the field. Not for verifying historical priority. I would cite it for the resource table and the planning taxonomy, not for its 'first' claims.

If I were the editor, I would send this to peer review and ask for revisions: add a methodology section, fix the attribution, and substantiate or qualify the priority claims. As it stands, it is a useful survey with a few trust issues that are fixable.

Referee Report

4 major / 5 minor

Summary. This survey reviews LiDAR-based autonomous UAV systems across sensing hardware, perception (state estimation, occupancy mapping, dynamic object detection), planning and control, open-source resources, and applications. It argues that LiDAR is a key enabler of high-speed and GPS-denied autonomy, and it makes several categorical claims: that a dedicated comprehensive review has been absent, that the authors' PULSAR platform is the only self-rotating UAV achieving full autonomous navigation, and that reference [205] is the first tail-sitter VTOL UAV with fully autonomous flight. The paper is organized as a conventional narrative survey with figures, a taxonomy of planning approaches, and a table of open-source projects, ending with future directions.

Significance. If the survey's coverage is reliable, it would serve as a useful entry point to a fast-moving area and a convenient condensation of sensor, algorithm, and platform developments. The paper has tangible strengths: a clear structure, a broad reference list, a useful tabulation of open-source resources with links, and an accessible discussion of how LiDAR characteristics interact with UAV-specific constraints. The authors are well-positioned to write such a survey, and several described systems come with publicly available code. The value, however, depends on the accuracy and representativeness of the literature summaries, because the survey's main contribution is the map itself rather than new technical results. The current manuscript does not yet provide enough methodological transparency or historical accuracy to fully support the categorical 'first/only/comprehensive' claims.

major comments (4)

[I; Fig. 1] The paper's central claim is that it fills a gap by being a 'dedicated and comprehensive review' of LiDAR-based autonomous UAVs. No search protocol, inclusion criteria, or screening process is documented. The only quantitative support, Fig. 1, uses the Web of Science query ("LiDAR" OR "Laser") AND ("UAV" OR "MAV" OR "Drone" OR "Aerial"), which counts broad LiDAR-UAV publications, not specifically autonomous systems, and is too coarse to substantiate a 'comprehensive' coverage claim. I recommend adding a methodology paragraph that states databases, query strings, time span, inclusion/exclusion criteria, and screening counts, or softening the claim to 'a representative review of recent work.'
[VI-B-1; VI-B-2] Categorical priority claims are load-bearing but rest on a small, non-systematic reference base. Section VI-B-1 calls PULSAR [161] 'the only one exception' among self-rotating autonomous UAVs, but the comparison considers only [198]–[202] and then [203]–[204]. Section VI-B-2 states that [205] is 'the first tail-sitter VTOL UAV that achieves fully autonomous flight ability,' while the discussion mentions only [206] and [207]. No evidence is provided that the search covered earlier or concurrent work, especially in the aeronautics and control literature. These statements should be softened to 'to the best of our knowledge' or supported by a documented systematic search with an explicit enumeration of the candidate set.
[III-A-1] The historical state-estimation timeline contains a clear factual error: the text says 'In 1998, Miller et al. used a 2D laser range finder for environmental mapping,' but no Miller et al. 1998 appears in the bibliography; the 1998 mapping work discussed in the surrounding context is Ryan [37], '3-D site mapping with the CMU autonomous helicopter.' The next sentence dates Thrun et al. [22] to 'By 2003,' but reference [22] is the 2006 Field and Service Robotics paper 'Scan alignment and 3-D surface modeling with a helicopter platform.' Because this subsection is explicitly historical, these errors undermine confidence in the accuracy of the per-paper summaries. The attribution and date must be corrected, and the other timeline claims should be rechecked.
[VI; Table I] The milestone and open-source selections are heavily weighted toward the authors' own laboratory: PULSAR [161], the tail-sitter system [205], IPC [145], ROG-Map [48], D-Map [49], FAST-LIO/FAST-LIVO, Swarm-LIO2 [62], and MARSIM, among others. This is not itself an error, but when the same self-citations are used both to define the milestones and to support the 'only/first' and 'comprehensive' claims, there is a risk of circularity in the evidence selection. I ask the authors to state the criteria by which systems were chosen for these sections and, where possible, to include independent or competing works so the reader can judge representativeness.

minor comments (5)

[III-B-1] Heading typo: 'Occupancy Rerepsentations' should be 'Occupancy Representations.'
[IV-A-2-b] Duplicate phrase: 'building and updating the ESDF-map can be can be computationally heavy.'
[VI-B-2] Typos: 'clusterd environments,' 'ourdoor park,' and 'compete autonomous flight' (the last also in VI-B-1) should read 'cluttered,' 'outdoor,' and 'complete.'
[Table I] Several rows lack references or are not clearly LiDAR-based: Fast-Drone-250, Agilicious, and OmniNxt are labeled Vision-based; A-LOAM and Geometry Control have no reference. Clarify the inclusion criteria and add citations or notes.
[Fig. 1 caption] The caption says the plot counts 'publications related to LiDAR-based UAV,' but the query does not include 'autonomous.' The count therefore likely includes many non-autonomous LiDAR-UAV papers; the caption should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: survey has no mathematical derivation; its 'first/only' claims are literature assertions supported by primary citations, not reductions to the paper's own inputs.

full rationale

This is a survey paper with no equations, fitted parameters, or predictive derivations, so there is no formal chain in which a result is equivalent to its input by construction. The strongest claims are literature-scope assertions: that a dedicated comprehensive review of LiDAR-based autonomous UAVs 'remains absent' (Sec. I), that PULSAR is 'the only one exception' among self-rotating autonomous UAVs (Sec. VI-B-1), and that [205] is 'the first tail-sitter VTOL UAV that achieves fully autonomous flight ability' (Sec. VI-B-2). These claims cite the authors' own previous publications, but the citations point to peer-reviewed, externally checkable systems (e.g., Science Robotics and IEEE T-RO); the survey does not derive those priority claims from the citations themselves, nor does it use a self-citation as a uniqueness theorem to forbid alternatives. The absence of a documented search protocol is a methodological completeness limitation, not circular reasoning. The factual misattribution in Sec. III-A-1 (a 1998 mapping effort attributed to 'Miller et al.' with reference [37] being M. Ryan's CMU helicopter paper) undermines confidence in the survey's summaries, but it is an accuracy issue, not a circularity issue. Therefore no circular steps are identified and the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

Surveys introduce no free parameters or entities. The load-bearing intellectual premises are selection representativeness and faithful transmission of the cited literature; both are only partially supported.

assumptions (3)

domain assumption The Web of Science search with the stated terms ('LiDAR' OR 'Laser') AND ('UAV' OR 'MAV' OR 'Drone' OR 'Aerial') captures the relevant population of LiDAR-based UAV publications.
Figure 1's publication counts rely on this search; no query date, deduplication, or validation against another database is given.
domain assumption The curated selection of works and open-source projects is representative of the state of the art.
Sections III-VI make general statements about methods and 'firsts' based on a non-systematic, self-overlapping set of references; no inclusion/exclusion criteria are stated. Part of Table I is adapted from [179], so it is not an independent audit.
domain assumption Per-paper descriptions in the survey are faithful to the original sources.
The paper itself contains attribution errors (e.g., 'Miller et al.' for [37] Ryan; 'By 2003, Thrun et al. [22]' but [22] is 2006), so this assumption is not fully met.

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Cite this review

Pith. "Pith review of A Survey on LiDAR-based Autonomous Aerial Vehicles." pith.science (2026). https://pith.science/paper/FW7HYYKA

@misc{pith2026250910730,
  author       = {Pith},
  title        = {Pith review of: A Survey on LiDAR-based Autonomous Aerial Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FW7HYYKA}},
  note         = {Machine review of arXiv:2509.10730}
}

read the original abstract

This survey offers a comprehensive overview of recent advancements in LiDAR-based autonomous Unmanned Aerial Vehicles (UAVs), covering their design, perception, planning, and control strategies. Over the past decade, LiDAR technology has become a crucial enabler for high-speed, agile, and reliable UAV navigation, especially in GPS-denied environments. The paper begins by examining the evolution of LiDAR sensors, emphasizing their unique advantages such as high accuracy, long-range depth measurements, and robust performance under various lighting conditions, making them particularly well-suited for UAV applications. The integration of LiDAR with UAVs has significantly enhanced their autonomy, enabling complex missions in diverse and challenging environments. Subsequently, we explore essential software components, including perception technologies for state estimation and mapping, as well as trajectory planning and control methodologies, and discuss their adoption in LiDAR-based UAVs. Additionally, we analyze various practical applications of the LiDAR-based UAVs, ranging from industrial operations to supporting different aerial platforms and UAV swarm deployments. The survey concludes by discussing existing challenges and proposing future research directions to advance LiDAR-based UAVs and enhance multi-UAV collaboration. By synthesizing recent developments, this paper aims to provide a valuable resource for researchers and practitioners working to push the boundaries of LiDAR-based UAV systems.

Figures

Figures reproduced from arXiv: 2509.10730 by the authors.

**Figure 1.** (A) Number of publications on LiDAR-based UAV since 2000. In 2021, the number of publications related to LiDAR-based UAV surpassed one thousand. These data were retrieved from the Web of Science, using the search terms: (“LiDAR” OR “Laser”) AND (“UAV” OR “MAV” OR “Drone” OR “Aerial”). (B) The architecture of this paper. in adverse weather and offers long-range detection with low weight and power needs. Vision-based … view at source ↗

**Figure 2.** Overview of LiDAR developments and corresponding UAV demonstrations in recent years, including Sick LMS 2D LiDAR [ [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Illustration of four notable state estimation methods: ( [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Existing planning approaches can be categorized into two types: ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: LiDAR’s application on different UAV platforms. ( [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: Summary and classification of future directions. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

219 extracted references · 4 linked inside Pith

[37]

3-d site mapping with the cmu autonomous helicopter,

M. Ryan, “3-d site mapping with the cmu autonomous helicopter,” in The 5th International Conference on Intelligent Autonomous Systems, 1998, 1998

1998
[205]

Autonomous tail-sitter flights in unknown environments,

G. Lu, Y . Ren, F. Zhu, H. Li, R. Xue, Y . Cai, X. Lyu, and F. Zhang, “Autonomous tail-sitter flights in unknown environments,”IEEE Trans- actions on Robotics, vol. 41, pp. 1098–1117, 2025

2025
[161]

A self-rotating, single-actuated uav with extended sensor field of view for autonomous navigation,

N. Chen, F. Kong, W. Xu, Y . Cai, H. Li, D. He, Y . Qin, and F. Zhang, “A self-rotating, single-actuated uav with extended sensor field of view for autonomous navigation,”Science Robotics, vol. 8, no. 76, p. eade4538, 2023

2023
[198]

Piccolissimo: The smallest micro aerial vehicle,

M. Piccoli and M. Yim, “Piccolissimo: The smallest micro aerial vehicle,” in2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017, pp. 3328–3333

2017
[202]

Splitflyer air: A modular quadcopter that disassembles into two bicopters mid-air,

S. Bai and P. Chirarattananon, “Splitflyer air: A modular quadcopter that disassembles into two bicopters mid-air,”IEEE/ASME Transac- tions on Mechatronics, vol. 27, no. 6, pp. 4729–4740, 2022

2022
[203]

Lockheed martin’s samarai nano air vehicle: challenges, research, and realization,

S. Jameson, K. Fregene, M. Chang, N. Allen, H. Youngren, and J. Scroggins, “Lockheed martin’s samarai nano air vehicle: challenges, research, and realization,” in50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 2012, p. 584

2012
[204]

Autonomous quadrotor flight despite rotor failure with onboard vision sensors: Frames vs. events,

S. Sun, G. Cioffi, C. De Visser, and D. Scaramuzza, “Autonomous quadrotor flight despite rotor failure with onboard vision sensors: Frames vs. events,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 580–587, 2021

2021
[206]

Swashplateless-elevon actuation for a dual-rotor tail-sitter vtol uav,

N. Chen, F. Kong, H. Li, J. Liu, Z. Ye, W. Xu, F. Zhu, X. Lyu, and F. Zhang, “Swashplateless-elevon actuation for a dual-rotor tail-sitter vtol uav,” in2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023, pp. 6970–6976

2023
[207]

Trajectory generation and tracking control for aggressive tail-sitter flights,

G. Lu, Y . Cai, N. Chen, F. Kong, Y . Ren, and F. Zhang, “Trajectory generation and tracking control for aggressive tail-sitter flights,”The International Journal of Robotics Research, vol. 43, no. 3, pp. 241–280, 2024

2024
[22]

Scan alignment and 3-d surface modeling with a helicopter platform,

S. Thrun, M. Diel, and D. H ¨ahnel, “Scan alignment and 3-d surface modeling with a helicopter platform,” inField and Service Robotics: Recent Advances in Reserch and Applications. Springer, 2006, pp. 287–297

2006
[145]

Integrated planning and control for quadrotor navigation in presence of suddenly appearing objects and disturbances,

W. Liu, Y . Ren, and F. Zhang, “Integrated planning and control for quadrotor navigation in presence of suddenly appearing objects and disturbances,”IEEE Robotics and Automation Letters, pp. 1–8, 2023

2023
[48]

Rog-map: An efficient robocentric occupancy grid map for large-scene and high-resolution lidar-based motion planning,

Y . Ren, Y . Cai, F. Zhu, S. Liang, and F. Zhang, “Rog-map: An efficient robocentric occupancy grid map for large-scene and high-resolution lidar-based motion planning,” in2024 IEEE/RSJ International Con- ference on Intelligent Robots and Systems (IROS). IEEE, 2024, pp. 8119–8125

2024
[49]

Occupancy grid mapping without ray-casting for high-resolution lidar sensors,

Y . Cai, F. Kong, Y . Ren, F. Zhu, J. Lin, and F. Zhang, “Occupancy grid mapping without ray-casting for high-resolution lidar sensors,”IEEE Transactions on Robotics, 2023

2023
[62]

Swarm-lio2: Decentralized, efficient lidar-inertial odometry for uav swarms,

F. Zhu, Y . Ren, L. Yin, F. Kong, Q. Liu, R. Xue, W. Liu, Y . Cai, G. Lu, H. Li,et al., “Swarm-lio2: Decentralized, efficient lidar-inertial odometry for uav swarms,”IEEE Transactions on Robotics, vol. 41, pp. 960–981, 2024

2024

Show all 219 references

[1]

Review of photogrammetric and lidar applications of uav,

L. Kovani ˇc, B. Topitzer, P. Pet’ovsk `y, P. Bli ˇst’an, M. B. Gergel’ov ´a, and M. Bliˇst’anov´a, “Review of photogrammetric and lidar applications of uav,”Applied Sciences, vol. 13, no. 11, p. 6732, 2023

2023
[2]

A review on uav-based applications for precision agriculture,

D. C. Tsouros, S. Bibi, and P. G. Sarigiannidis, “A review on uav-based applications for precision agriculture,”Information, vol. 10, no. 11, pp. 349–374, 2019

2019
[3]

State-of-the-art technologies for uav inspections,

S. Jordan, J. Moore, S. Hovet, J. Box, J. Perry, K. Kirsche, D. Lewis, and Z. T. H. Tse, “State-of-the-art technologies for uav inspections,” IET Radar, Sonar & Navigation, vol. 12, no. 2, pp. 151–164, 2018

2018
[4]

Applications of drone in disaster management: A scoping review,

S. M. S. M. Daud, M. Y . P. M. Yusof, C. C. Heo, L. S. Khoo, M. K. C. Singh, M. S. Mahmood, and H. Nawawi, “Applications of drone in disaster management: A scoping review,”Science & Justice, vol. 62, no. 1, pp. 30–42, 2022

2022
[5]

A drone fleet model for last- mile distribution in disaster relief operations,

B. Rabta, C. Wankm ¨uller, and G. Reiner, “A drone fleet model for last- mile distribution in disaster relief operations,”International Journal of Disaster Risk Reduction, vol. 28, pp. 107–112, 2018

2018
[6]

Radar odometry for au- tonomous ground vehicles: A survey of methods and datasets,

N. J. Abu-Alrub and N. A. Rawashdeh, “Radar odometry for au- tonomous ground vehicles: A survey of methods and datasets,”IEEE Transactions on Intelligent Vehicles, vol. 9, no. 3, pp. 4275–4291, 2023

2023
[7]

Vision-based state estimation and trajectory control towards high-speed flight with a quadrotor

S. Shen, Y . Mulgaonkar, N. Michael, and V . Kumar, “Vision-based state estimation and trajectory control towards high-speed flight with a quadrotor.” inRobotics: Science and Systems, vol. 1. Citeseer, 2013, p. 32

2013
[8]

Safe local exploration for replanning in cluttered unknown environments for microaerial vehicles,

H. Oleynikova, Z. Taylor, R. Siegwart, and J. Nieto, “Safe local exploration for replanning in cluttered unknown environments for microaerial vehicles,”IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1474–1481, 2018

2018
[9]

Robust and efficient quadrotor trajectory generation for fast autonomous flight,

B. Zhou, F. Gao, L. Wang, C. Liu, and S. Shen, “Robust and efficient quadrotor trajectory generation for fast autonomous flight,”IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3529–3536, 2019

2019
[10]

Safety-assured high-speed navigation for mavs,

Y . Ren, F. Zhu, G. Lu, Y . Cai, L. Yin, F. Kong, J. Lin, N. Chen, and F. Zhang, “Safety-assured high-speed navigation for mavs,”Science Robotics, vol. 10, no. 98, p. eado6187, 2025

2025
[11]

Online whole-body motion planning for quadrotor using multi-resolution search,

Y . Ren, S. Liang, F. Zhu, G. Lu, and F. Zhang, “Online whole-body motion planning for quadrotor using multi-resolution search,” in2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 1594–1600

2023
[12]

Fast-lio2: Fast direct lidar-inertial odometry,

W. Xu, Y . Cai, D. He, J. Lin, and F. Zhang, “Fast-lio2: Fast direct lidar-inertial odometry,”IEEE Transactions on Robotics, 2022

2022
[13]

Point-lio: Robust high-bandwidth light detection and ranging inertial odometry,

D. He, W. Xu, N. Chen, F. Kong, C. Yuan, and F. Zhang, “Point-lio: Robust high-bandwidth light detection and ranging inertial odometry,” Advanced Intelligent Systems, vol. 5, no. 7, p. 2200459, 2023

2023
[14]

Dynamic control barrier function-based model predictive control to safety-critical obstacle-avoidance of mobile robot,

Z. Jian, Z. Yan, X. Lei, Z. Lu, B. Lan, X. Wang, and B. Liang, “Dynamic control barrier function-based model predictive control to safety-critical obstacle-avoidance of mobile robot,” in2023 IEEE International Conference on Robotics and Automation (ICRA). Ieee, 2023, pp. 3679–3685

2023
[15]

Learning quadrupedal locomotion over challenging terrain,

J. Lee, J. Hwangbo, L. Wellhausen, V . Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,”Science robotics, vol. 5, no. 47, p. eabc5986, 2020

2020
[16]

Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems,

F. Kendoul, “Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems,”Journal of Field Robotics, vol. 29, no. 2, pp. 315–378, 2012

2012
[17]

Survey on computer vision for uavs: Current developments and trends,

C. Kanellakis and G. Nikolakopoulos, “Survey on computer vision for uavs: Current developments and trends,”Journal of Intelligent & Robotic Systems, vol. 87, pp. 141–168, 2017

2017
[18]

A review of gnss-independent uav navigation techniques,

N. Gyagenda, J. V . Hatilima, H. Roth, and V . Zhmud, “A review of gnss-independent uav navigation techniques,”Robotics and Au- tonomous Systems, vol. 152, p. 104069, 2022

2022
[19]

A review of uav autonomous navigation in gps-denied environments,

Y . Chang, Y . Cheng, U. Manzoor, and J. Murray, “A review of uav autonomous navigation in gps-denied environments,”Robotics and Autonomous Systems, p. 104533, 2023

2023
[20]

Lidar odometry survey: recent advancements and remaining challenges,

D. Lee, M. Jung, W. Yang, and A. Kim, “Lidar odometry survey: recent advancements and remaining challenges,”Intelligent Service Robotics, vol. 17, no. 2, pp. 95–118, 2024

2024
[21]

Lidar-based place recognition for au- tonomous driving: A survey,

Y . Zhang, P. Shi, and J. Li, “Lidar-based place recognition for au- tonomous driving: A survey,”ACM Computing Surveys, vol. 57, no. 4, pp. 1–36, 2024

2024
[23]

P-cap: Pre- computed alternative paths to enable aggressive aerial maneuvers in cluttered environments,

J. Zhang, R. G. Chadha, V . Velivela, and S. Singh, “P-cap: Pre- computed alternative paths to enable aggressive aerial maneuvers in cluttered environments,” in2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018, pp. 8456– 8463

2018
[24]

Evaluation of uav lidar for mapping coastal environments,

Y .-C. Lin, Y .-T. Cheng, T. Zhou, R. Ravi, S. M. Hasheminasab, J. E. Flatt, C. Troy, and A. Habib, “Evaluation of uav lidar for mapping coastal environments,”Remote Sensing, vol. 11, no. 24, p. 2893, 2019

2019
[25]

Geometrically constrained tra- jectory optimization for multicopters,

Z. Wang, X. Zhou, C. Xu, and F. Gao, “Geometrically constrained tra- jectory optimization for multicopters,”IEEE Transactions on Robotics, vol. 38, no. 5, pp. 3259–3278, 2022

2022
[26]

Treescope: An agricultural robotics dataset for lidar-based mapping of trees in forests and or- chards,

D. Cheng, F. Cladera, A. Prabhu, X. Liu, A. Zhu, P. C. Green, R. Ehsani, P. Chaudhari, and V . Kumar, “Treescope: An agricultural robotics dataset for lidar-based mapping of trees in forests and or- chards,” in2024 IEEE International Conference on Robotics and Automation (ICRA...

2024
[27]

An autonomous uav system based on adaptive lidar inertial odometry for practical exploration in complex environments,

B. Kim, M. B. Azhari, J. Park, and D. H. Shim, “An autonomous uav system based on adaptive lidar inertial odometry for practical exploration in complex environments,”Journal of Field Robotics, vol. 41, no. 3, pp. 669–698, 2024

2024
[28]

Avoiding dynamic small obstacles with onboard sensing and computation on aerial robots,

F. Kong, W. Xu, Y . Cai, and F. Zhang, “Avoiding dynamic small obstacles with onboard sensing and computation on aerial robots,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7869–7876, 2021

2021
[29]

Mars-lvig dataset: A multi-sensor aerial robots slam dataset for lidar-visual-inertial-gnss fusion,

H. Li, Y . Zou, N. Chen, J. Lin, X. Liu, W. Xu, C. Zheng, R. Li, D. He, F. Kong,et al., “Mars-lvig dataset: A multi-sensor aerial robots slam dataset for lidar-visual-inertial-gnss fusion,”The International Journal of Robotics Research, p. 02783649241227968, 2024

2024
[30]

Bubble planner: Planning high-speed smooth quadrotor trajectories using receding corridors,

Y . Ren, F. Zhu, W. Liu, Z. Wang, Y . Lin, F. Gao, and F. Zhang, “Bubble planner: Planning high-speed smooth quadrotor trajectories using receding corridors,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 6332–6339

2022
[31]

Slope inspection under dense vegetation using lidar-based quadrotors,

W. Liu, Y . Ren, R. Guo, V . W. Kong, A. S. Hung, F. Zhu, Y . Cai, H. Wu, Y . Zou, and F. Zhang, “Slope inspection under dense vegetation using lidar-based quadrotors,”Nature Communications, vol. 16, no. 1, pp. 7411–7424, 2025

2025
[32]

Research on vision-based servoing and trajectory prediction strategy for capturing illegal drones,

J. Ma, P. Chen, X. Xiong, L. Zhang, S. Yu, and D. Zhang, “Research on vision-based servoing and trajectory prediction strategy for capturing illegal drones,”Drones, vol. 8, no. 4, pp. 127–143, 2024

2024
[33]

A robust lidar- based indoor exploration framework for uavs in uncooperative envi- ronments,

W. Bolz, M. Cella, N. Maikisch, F. Vultaggio, F. d’Apolito, F. Bruckm ¨uller, C. Sulzbachner, and P. Fanta-Jende, “A robust lidar- based indoor exploration framework for uavs in uncooperative envi- ronments,” in2024 International Conference on Unmanned Aircraft Systems (ICUAS)...

2024
[34]

Air bumper: A collision detection and reaction framework for autonomous mav 13 navigation,

R. Wang, Z. Guo, Y . Chen, X. Wang, and B. M. Chen, “Air bumper: A collision detection and reaction framework for autonomous mav 13 navigation,” in2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 15 735–15 741

2024
[35]

Developing a flying explorer for autonomous digital modelling in wild unknowns,

N. Zhang, Y . Pan, Y . Jin, P. Jin, K. Hu, X. Huang, and H. Kang, “Developing a flying explorer for autonomous digital modelling in wild unknowns,”Sensors, vol. 24, no. 3, p. 1021, 2024

2024
[36]

Optical ranging system uses laser transmitter,

M. Stitch, E. Woodbury, and J. Morse, “Optical ranging system uses laser transmitter,”Electronics, vol. 34, pp. 51–53, 1961

1961
[38]

Conflict-free navigation in unknown urban environments,

D. H. Shim, H. Chung, and S. S. Sastry, “Conflict-free navigation in unknown urban environments,”IEEE robotics & automation magazine, vol. 13, no. 3, pp. 27–33, 2006

2006
[39]

Flight trials of a rotorcraft unmanned aerial vehicle landing autonomously at unprepared sites,

C. Theodore, D. Rowley, A. Ansar, L. Matthies, S. Goldberg, D. Hub- bard, and M. Whalley, “Flight trials of a rotorcraft unmanned aerial vehicle landing autonomously at unprepared sites,” inAnnual forum proceedings-American helicopter society, vol. 62, no. 2. AMERICAN HELICOPT...

2006
[40]

Little ben: The ben franklin racing team’s entry in the 2007 darpa urban challenge,

J. Bohren, T. Foote, J. Keller, A. Kushleyev, D. Lee, A. Stewart, P. Vernaza, J. Derenick, J. Spletzer, and B. Satterfield, “Little ben: The ben franklin racing team’s entry in the 2007 darpa urban challenge,” Journal of Field Robotics, vol. 25, no. 9, pp. 598–614, 2008

2007
[41]

Online quadrotor trajectory generation and autonomous navigation on point clouds,

F. Gao and S. Shen, “Online quadrotor trajectory generation and autonomous navigation on point clouds,” in2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2016, pp. 139–146

2016
[42]

Falco: Fast likelihood- based collision avoidance with extension to human-guided navigation,

J. Zhang, C. Hu, R. G. Chadha, and S. Singh, “Falco: Fast likelihood- based collision avoidance with extension to human-guided navigation,” Journal of Field Robotics, vol. 37, no. 8, pp. 1300–1313, 2020

2020
[43]

Ouster OS1-128 sensor,

“Ouster OS1-128 sensor,” https://ouster.com/products/scanning-lidar/ os1-sensor, 2024. [Online]. Available: https://ouster.com/products/ scanning-lidar/os1-sensor

2024
[44]

Livox Avia LiDAR,

“Livox Avia LiDAR,” https://www.livoxtech.com/avia/specs, 2024. [Online]. Available: https://www.livoxtech.com/avia/specs [45]Livox Mid-360 User Manual, Livox Technology Company Limited, Jan. 2023, https://www.livoxtech.com/mid-360

2024
[46]

Fmcw lidar system to reduce hard- ware complexity and post-processing techniques to improve distance resolution,

C. Kim, Y . Jung, and S. Lee, “Fmcw lidar system to reduce hard- ware complexity and post-processing techniques to improve distance resolution,”Sensors, vol. 20, no. 22, pp. 6676–6687, 2020

2020
[47]

Fully integrated fmcw lidar op- tical engine on a single silicon chip,

K. Sayyah, R. Sarkissian, P. Patterson, B. Huang, O. Efimov, D. Kim, K. Elliott, L. Yang, and D. Hammon, “Fully integrated fmcw lidar op- tical engine on a single silicon chip,”Journal of Lightwave Technology, vol. 40, no. 9, pp. 2763–2772, 2022

2022
[50]

On-manifold model predictive control for trajectory tracking on robotic systems,

G. Lu, W. Xu, and F. Zhang, “On-manifold model predictive control for trajectory tracking on robotic systems,”IEEE Transactions on Industrial Electronics, pp. 1–10, 2022

2022
[51]

Loam: Lidar odometry and mapping in real- time

J. Zhang and S. Singh, “Loam: Lidar odometry and mapping in real- time.” inRobotics: Science and Systems, vol. 2, no. 9. Berkeley, CA, 2014, pp. 1–9

2014
[52]

Fast-livo2: Fast, direct lidar-inertial-visual odometry,

C. Zheng, W. Xu, Z. Zou, T. Hua, C. Yuan, D. He, B. Zhou, Z. Liu, J. Lin, F. Zhu,et al., “Fast-livo2: Fast, direct lidar-inertial-visual odometry,”IEEE Transactions on Robotics, vol. 41, pp. 326–346, 2024

2024
[53]

Autonomous in- door hovering with a quadrotor,

G. Angeletti, J. P. Valente, L. Iocchi, and D. Nardi, “Autonomous in- door hovering with a quadrotor,” inWorkshop Proc. SIMPAR. Citeseer, 2008, pp. 472–481

2008
[54]

Planning in information space for a quadrotor helicopter in a gps-denied environment,

R. He, S. Prentice, and N. Roy, “Planning in information space for a quadrotor helicopter in a gps-denied environment,” in2008 IEEE International conference on Robotics and Automation. IEEE, 2008, pp. 1814–1820

2008
[55]

Efficient planning under uncertainty for a target-tracking micro-aerial vehicle,

R. He, A. Bachrach, and N. Roy, “Efficient planning under uncertainty for a target-tracking micro-aerial vehicle,” in2010 IEEE International Conference on Robotics and Automation. IEEE, 2010, pp. 1–8

2010
[56]

State estimation for aggressive flight in gps-denied environments using onboard sensing,

A. Bry, A. Bachrach, and N. Roy, “State estimation for aggressive flight in gps-denied environments using onboard sensing,” in2012 IEEE international conference on robotics and automation. IEEE, 2012, pp. 1–8

2012
[57]

Method for registration of 3-d shapes,

P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes,” inSensor fusion IV: control paradigms and data structures, vol. 1611. Spie, 1992, pp. 586–606

1992
[58]

Real-time loop closure in 2d lidar slam,

W. Hess, D. Kohler, H. Rapp, and D. Andor, “Real-time loop closure in 2d lidar slam,” in2016 IEEE international conference on robotics and automation (ICRA). IEEE, 2016, pp. 1271–1278

2016
[59]

ikd-tree: An incremental kd tree for robotic applications,

Y . Cai, W. Xu, and F. Zhang, “ikd-tree: An incremental kd tree for robotic applications,”arXiv preprint arXiv:2102.10808, 2021

2021 arXiv
[60]

Robust real-time lidar-inertial initializa- tion,

F. Zhu, Y . Ren, and F. Zhang, “Robust real-time lidar-inertial initializa- tion,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 3948–3955

2022
[61]

Swarm-lio: Decentralized swarm lidar-inertial odometry,

F. Zhu, Y . Ren, F. Kong, H. Wu, S. Liang, N. Chen, W. Xu, and F. Zhang, “Swarm-lio: Decentralized swarm lidar-inertial odometry,” in2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 3254–3260

2023
[63]

Efficient and prob- abilistic adaptive voxel mapping for accurate online lidar odometry,

C. Yuan, W. Xu, X. Liu, X. Hong, and F. Zhang, “Efficient and prob- abilistic adaptive voxel mapping for accurate online lidar odometry,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 8518–8525, 2022

2022
[64]

Fast-livo: Fast and tightly-coupled sparse-direct lidar-inertial-visual odometry,

C. Zheng, Q. Zhu, W. Xu, X. Liu, Q. Guo, and F. Zhang, “Fast-livo: Fast and tightly-coupled sparse-direct lidar-inertial-visual odometry,” in2022 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2022, pp. 4003–4009

2022
[65]

Suma++: Efficient lidar-based semantic slam,

X. Chen, A. Milioto, E. Palazzolo, P. Giguere, J. Behley, and C. Stach- niss, “Suma++: Efficient lidar-based semantic slam,” in2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019, pp. 4530–4537

2019
[66]

Sloam: Semantic lidar odometry and mapping for forest inventory,

S. W. Chen, G. V . Nardari, E. S. Lee, C. Qu, X. Liu, R. A. F. Romero, and V . Kumar, “Sloam: Semantic lidar odometry and mapping for forest inventory,”IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 612–619, 2020

2020
[67]

Velodyne VLP-16 Lite LiDAR,

“Velodyne VLP-16 Lite LiDAR,” https://velodynelidar.com/products/ puck-lite/, 2024. [Online]. Available: https://velodynelidar.com/ products/puck/

2024
[68]

Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-d complex environments,

S. Liu, M. Watterson, K. Mohta, K. Sun, S. Bhattacharya, C. J. Taylor, and V . Kumar, “Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-d complex environments,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1688–1695, 2017

2017
[69]

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments,

F. Gao, W. Wu, W. Gao, and S. Shen, “Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments,”Journal of Field Robotics, vol. 36, no. 4, pp. 710–733, 2019

2019
[70]

P-cap: Pre- computed alternative paths to enable aggressive aerial maneuvers in cluttered environments,

J. Zhang, R. G. Chadha, V . Velivela, and S. Singh, “P-cap: Pre- computed alternative paths to enable aggressive aerial maneuvers in cluttered environments,” in2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 8456–8463

2018
[71]

P-cal: Pre-computed alternative lanes for aggressive aerial collision avoidance,

——, “P-cal: Pre-computed alternative lanes for aggressive aerial collision avoidance,” inThe 12th International Conference on Field and Service Robotics (FSR), 2019

2019
[72]

Efficient surfel-based slam using 3d laser range data in urban environments

J. Behley and C. Stachniss, “Efficient surfel-based slam using 3d laser range data in urban environments.” inRobotics: science and systems, vol. 2018, 2018, p. 59

2018
[73]

Tightly coupled 3d lidar inertial odometry and mapping,

H. Ye, Y . Chen, and M. Liu, “Tightly coupled 3d lidar inertial odometry and mapping,” in2019 international conference on robotics and automation (ICRA). IEEE, 2019, pp. 3144–3150

2019
[74]

Robust odometry and mapping for multi-lidar systems with online extrinsic calibration,

J. Jiao, H. Ye, Y . Zhu, and M. Liu, “Robust odometry and mapping for multi-lidar systems with online extrinsic calibration,”IEEE Trans- actions on Robotics, vol. 38, no. 1, pp. 351–371, 2021

2021
[75]

Low-cost retina-like robotic lidars based on incommensurable scanning,

Z. Liu, F. Zhang, and X. Hong, “Low-cost retina-like robotic lidars based on incommensurable scanning,”IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 58–68, 2021

2021
[76]

Loam livox: A fast, robust, high-precision lidar odometry and mapping package for lidars of small fov,

J. Lin and F. Zhang, “Loam livox: A fast, robust, high-precision lidar odometry and mapping package for lidars of small fov,” in2020 IEEE international conference on robotics and automation (ICRA). IEEE, 2020, pp. 3126–3131

2020
[77]

Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter,

W. Xu and F. Zhang, “Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3317–3324, 2021

2021
[78]

Bubble explorer: Fast uav exploration in large-scale and cluttered 3d-environments using occlusion-free spheres,

B. Tang, Y . Ren, F. Zhu, R. He, S. Liang, F. Kong, and F. Zhang, “Bubble explorer: Fast uav exploration in large-scale and cluttered 3d-environments using occlusion-free spheres,” in2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023, p...

2023
[79]

Time delay error online correction of lidar-imu system through msckf integrated dlrnn method,

W. Liu, Z. Li, W. Li, P. Gardoni, H. Du, and M. A. Sotelo, “Time delay error online correction of lidar-imu system through msckf integrated dlrnn method,”IEEE/ASME Transactions on Mechatronics, 2023

2023
[80]

R3live++: A robust, real-time, radiance re- construction package with a tightly-coupled lidar-inertial-visual state estimator,

J. Lin and F. Zhang, “R3live++: A robust, real-time, radiance re- construction package with a tightly-coupled lidar-inertial-visual state estimator,”IEEE Transactions on Pattern Analysis and Machine Intel- ligence, 2024

2024
[81]

Continuous- time fixed-lag smoothing for lidar-inertial-camera slam,

J. Lv, X. Lang, J. Xu, M. Wang, Y . Liu, and X. Zuo, “Continuous- time fixed-lag smoothing for lidar-inertial-camera slam,”IEEE/ASME Transactions on Mechatronics, vol. 28, no. 4, pp. 2259–2270, 2023

2023
[82]

Decentralized swarm trajectory generation for lidar-based aerial tracking in cluttered envi- ronments,

L. Yin, F. Zhu, Y . Ren, F. Kong, and F. Zhang, “Decentralized swarm trajectory generation for lidar-based aerial tracking in cluttered envi- ronments,” in2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023, pp. 9285–9292

2023
[83]

Fusion of visual-inertial odometry with lidar relative localization for cooperative guidance of a micro-scale aerial vehicle,

V . Pritzl, M. Vrba, P. ˇStˇep´an, and M. Saska, “Fusion of visual-inertial odometry with lidar relative localization for cooperative guidance of a micro-scale aerial vehicle,”arXiv preprint arXiv:2306.17544, 2023

2023
[84]

Thrun, W

S. Thrun, W. Burgard, and D. Fox,Probabilistic Robotics. MIT Press, 2005

2005
[85]

Sonar-based real-world mapping and navigation,

A. Elfes, “Sonar-based real-world mapping and navigation,”IEEE Journal on Robotics and Automation, vol. 3, no. 3, pp. 249–265, 1987

1987
[86]

Sensor fusion in certainty grids for mobile robots,

H. P. Moravec, “Sensor fusion in certainty grids for mobile robots,”AI magazine, vol. 9, no. 2, pp. 61–61, 1988

1988
[87]

Octomap: An efficient probabilistic 3d mapping framework based on octrees,

A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Bur- gard, “Octomap: An efficient probabilistic 3d mapping framework based on octrees,”Autonomous robots, vol. 34, no. 3, pp. 189–206, 2013

2013
[88]

V oxblox: Incremental 3d euclidean signed distance fields for on-board mav planning,

H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto, “V oxblox: Incremental 3d euclidean signed distance fields for on-board mav planning,” in2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 1366–1373

2017
[89]

Fiesta: Fast incremental euclidean distance fields for online motion planning of aerial robots,

L. Han, F. Gao, B. Zhou, and S. Shen, “Fiesta: Fast incremental euclidean distance fields for online motion planning of aerial robots,” in2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019, pp. 4423–4430

2019
[90]

Learning polyline maps from range scan data acquired with mobile robots,

M. Veeck and W. Veeck, “Learning polyline maps from range scan data acquired with mobile robots,” in2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), vol. 2. IEEE, 2004, pp. 1065–1070

2004
[91]

Robotic mapping with polygonal random fields,

M. Paskin and S. Thrun, “Robotic mapping with polygonal random fields,”arXiv preprint arXiv:1207.1399, 2012

2012 arXiv
[92]

Gaussian process occupancy maps,

S. T. O’Callaghan and F. T. Ramos, “Gaussian process occupancy maps,”The International Journal of Robotics Research, vol. 31, no. 1, pp. 42–62, 2012

2012
[93]

Gpmap: A unified framework for robotic mapping based on sparse gaussian processes,

S. Kim and J. Kim, “Gpmap: A unified framework for robotic mapping based on sparse gaussian processes,” inField and service robotics. Springer, 2015, pp. 319–332

2015
[94]

Fast, accurate gaussian process occupancy maps via test-data octrees and nested bayesian fusion,

J. Wang and B. Englot, “Fast, accurate gaussian process occupancy maps via test-data octrees and nested bayesian fusion,” in2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 1003–1010

2016
[95]

Learning-aided 3-d occupancy mapping with bayesian generalized kernel inference,

K. Doherty, T. Shan, J. Wang, and B. Englot, “Learning-aided 3-d occupancy mapping with bayesian generalized kernel inference,”IEEE Transactions on Robotics, vol. 35, no. 4, pp. 953–966, 2019

2019
[96]

Oct-trees and their use in rep- resenting three-dimensional objects,

C. L. Jackins and S. L. Tanimoto, “Oct-trees and their use in rep- resenting three-dimensional objects,”Computer Graphics and Image Processing, vol. 14, no. 3, pp. 249–270, 1980

1980
[97]

3d normal distributions transform occupancy maps: An efficient repre- sentation for mapping in dynamic environments,

J. P. Saarinen, H. Andreasson, T. Stoyanov, and A. J. Lilienthal, “3d normal distributions transform occupancy maps: An efficient repre- sentation for mapping in dynamic environments,”The International Journal of Robotics Research, vol. 32, no. 14, pp. 1627–1644, 2013

2013
[98]

Confidence-rich grid mapping,

A.-A. Agha-Mohammadi, E. Heiden, K. Hausman, and G. Sukhatme, “Confidence-rich grid mapping,”The International Journal of Robotics Research, vol. 38, no. 12-13, pp. 1352–1374, 2019

2019
[99]

Hilbert maps: Scalable continuous occupancy mapping with stochastic gradient descent,

F. Ramos and L. Ott, “Hilbert maps: Scalable continuous occupancy mapping with stochastic gradient descent,”The International Journal of Robotics Research, vol. 35, no. 14, pp. 1717–1730, 2016

2016
[100]

Probabilistic map fusion for fast, incremental occupancy mapping with 3d hilbert maps,

K. Doherty, J. Wang, and B. Englot, “Probabilistic map fusion for fast, incremental occupancy mapping with 3d hilbert maps,” in2016 IEEE international conference on robotics and automation (ICRA). IEEE, 2016, pp. 1011–1018

2016
[101]

Large-scale 3d scene reconstruction with hilbert maps,

V . Guizilini and F. Ramos, “Large-scale 3d scene reconstruction with hilbert maps,” in2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016, pp. 3247–3254

2016
[102]

Continuous occupancy map fusion with fast bayesian hilbert maps,

W. Zhi, L. Ott, R. Senanayake, and F. Ramos, “Continuous occupancy map fusion with fast bayesian hilbert maps,” in2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019, pp. 4111–4117

2019
[103]

Efficient, multifidelity perceptual rep- resentations via hierarchical gaussian mixture models,

S. Srivastava and N. Michael, “Efficient, multifidelity perceptual rep- resentations via hierarchical gaussian mixture models,”IEEE Transac- tions on Robotics, vol. 35, no. 1, pp. 248–260, 2018

2018
[104]

Variable resolution occu- pancy mapping using gaussian mixture models,

C. O’Meadhra, W. Tabib, and N. Michael, “Variable resolution occu- pancy mapping using gaussian mixture models,”IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 2015–2022, 2018

2015
[105]

A universal grid map library: Imple- mentation and use case for rough terrain navigation,

P. Fankhauser and M. Hutter, “A universal grid map library: Imple- mentation and use case for rough terrain navigation,”Robot Operating System (ROS) The Complete Reference (Volume 1), pp. 99–120, 2016

2016
[106]

Real-time 3d reconstruction at scale using voxel hashing,

M. Nießner, M. Zollh ¨ofer, S. Izadi, and M. Stamminger, “Real-time 3d reconstruction at scale using voxel hashing,”ACM Transactions on Graphics (ToG), vol. 32, no. 6, pp. 1–11, 2013

2013
[107]

Ericson,Real-time collision detection

C. Ericson,Real-time collision detection. Crc Press, 2004

2004
[108]

Probabilistic quadtrees for variable-resolution mapping of large environments,

G. K. Kraetzschmar, G. P. Gassull, and K. Uhl, “Probabilistic quadtrees for variable-resolution mapping of large environments,”IFAC Proceed- ings Volumes, vol. 37, no. 8, pp. 675–680, 2004

2004
[109]

Hierarchies of octrees for efficient 3d mapping,

K. M. Wurm, D. Hennes, D. Holz, R. B. Rusu, C. Stachniss, K. Kono- lige, and W. Burgard, “Hierarchies of octrees for efficient 3d mapping,” in2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2011, pp. 4249–4255

2011
[110]

Efficient octree-based volumetric slam supporting signed-distance and occupancy mapping,

E. Vespa, N. Nikolov, M. Grimm, L. Nardi, P. H. Kelly, and S. Leutenegger, “Efficient octree-based volumetric slam supporting signed-distance and occupancy mapping,”IEEE Robotics and Automa- tion Letters, vol. 3, no. 2, pp. 1144–1151, 2018

2018
[111]

Ufomap: An efficient probabilistic 3d mapping framework that embraces the unknown,

D. Duberg and P. Jensfelt, “Ufomap: An efficient probabilistic 3d mapping framework that embraces the unknown,”IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6411–6418, 2020

2020
[112]

Multi-resolution 3d mapping with explicit free space representation for fast and accurate mobile robot motion planning,

N. Funk, J. Tarrio, S. Papatheodorou, M. Popovi ´c, P. F. Alcantarilla, and S. Leutenegger, “Multi-resolution 3d mapping with explicit free space representation for fast and accurate mobile robot motion planning,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3553–3...

2021
[113]

R-trees: A dynamic index structure for spatial searching,

A. Guttman, “R-trees: A dynamic index structure for spatial searching,” inProceedings of the 1984 ACM SIGMOD international conference on Management of data, 1984, pp. 47–57

1984
[114]

Gmmap: Memory-efficient continuous occupancy map using gaussian mixture model,

P. Z. X. Li, S. Karaman, and V . Sze, “Gmmap: Memory-efficient continuous occupancy map using gaussian mixture model,”IEEE Transactions on Robotics, 2024

2024
[115]

Motion-based detection and tracking in 3d lidar scans,

A. Dewan, T. Caselitz, G. D. Tipaldi, and W. Burgard, “Motion-based detection and tracking in 3d lidar scans,” in2016 IEEE international conference on robotics and automation (ICRA). IEEE, 2016, pp. 4508– 4513

2016
[116]

Mapless online detection of dynamic objects in 3d lidar,

D. Yoon, T. Tang, and T. Barfoot, “Mapless online detection of dynamic objects in 3d lidar,” in2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019, pp. 113–120

2019
[117]

Dufomap: Efficient dynamic awareness mapping,

D. Duberg, Q. Zhang, M. Jia, and P. Jensfelt, “Dufomap: Efficient dynamic awareness mapping,”IEEE Robotics and Automation Letters, 2024

2024
[118]

Moving event detection from lidar point streams,

H. Wu, Y . Li, W. Xu, F. Kong, and F. Zhang, “Moving event detection from lidar point streams,”Nature Communications, vol. 15, no. 1, p. 345, 2024

2024
[119]

Fapp: Fast and adaptive perception and planning for uavs in dynamic cluttered environments,

M. Lu, X. Fan, H. Chen, and P. Lu, “Fapp: Fast and adaptive perception and planning for uavs in dynamic cluttered environments,”IEEE Transactions on Robotics, 2024

2024
[120]

Receding moving object segmentation in 3d lidar data using sparse 4d convolutions,

B. Mersch, X. Chen, I. Vizzo, L. Nunes, J. Behley, and C. Stachniss, “Receding moving object segmentation in 3d lidar data using sparse 4d convolutions,”IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7503–7510, 2022

2022
[121]

Deflow: decoder of scene flow network in autonomous driving,

Q. Zhang, Y . Yang, H. Fang, R. Geng, and P. Jensfelt, “Deflow: decoder of scene flow network in autonomous driving,” in2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 2105–2111

2024
[122]

Intel Realsense Depth Camera D435i,

“Intel Realsense Depth Camera D435i,” https://www.intelrealsense. com/depth-camera-d435i, 2024. [Online]. Available: https://www. intelrealsense.com/depth-camera-d435i

2024
[123]

Randomized kinodynamic planning,

S. LaValle and J. Kuffner, “Randomized kinodynamic planning,” in Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), vol. 1. IEEE, 1999, pp. 473–479

1999
[124]

Kinodynamic rrt*: Asymptotically optimal motion planning for robots with linear dynamics,

D. J. Webb and J. Van Den Berg, “Kinodynamic rrt*: Asymptotically optimal motion planning for robots with linear dynamics,” in2013 IEEE international conference on robotics and automation. IEEE, 2013, pp. 5054–5061

2013
[125]

Asymptotically optimal sampling-based kinodynamic planning,

Y . Li, Z. Littlefield, and K. E. Bekris, “Asymptotically optimal sampling-based kinodynamic planning,”The International Journal of Robotics Research, vol. 35, no. 5, pp. 528–564, 2016. 15

2016
[126]

Planning long dynamically feasible maneuvers for autonomous vehicles,

M. Likhachev and D. Ferguson, “Planning long dynamically feasible maneuvers for autonomous vehicles,”The International Journal of Robotics Research, vol. 28, no. 8, pp. 933–945, 2009

2009
[127]

Path planning for non-circular micro aerial vehicles in constrained environments,

B. MacAllister, J. Butzke, A. Kushleyev, H. Pandey, and M. Likhachev, “Path planning for non-circular micro aerial vehicles in constrained environments,” in2013 ieee international conference on robotics and automation. IEEE, 2013, pp. 3933–3940

2013
[128]

Incremental micro-uav motion replanning for exploring unknown environments,

M. Pivtoraiko, D. Mellinger, and V . Kumar, “Incremental micro-uav motion replanning for exploring unknown environments,” in2013 IEEE international conference on robotics and automation. IEEE, 2013, pp. 2452–2458

2013
[129]

Search-based motion planning for quadrotors using linear quadratic minimum time control,

S. Liu, N. Atanasov, K. Mohta, and V . Kumar, “Search-based motion planning for quadrotors using linear quadratic minimum time control,” in2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2017, pp. 2872–2879

2017
[130]

Minimum snap trajectory generation and control for quadrotors,

D. Mellinger and V . Kumar, “Minimum snap trajectory generation and control for quadrotors,” in2011 IEEE International Conference on Robotics and Automation, 2011, pp. 2520–2525

2011
[131]

Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments,

C. Richter, A. Bry, and N. Roy, “Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments,” inRobotics Research: The 16th International Symposium ISRR. Springer, 2016, pp. 649–666

2016
[132]

Rapidly-exploring random trees: A new tool for path planning,

S. LaValle, “Rapidly-exploring random trees: A new tool for path planning,”Research Report 9811, 1998

1998
[133]

Mapless-planner: A robust and fast planning framework for aggressive autonomous flight without map fusion,

J. Ji, Z. Wang, Y . Wang, C. Xu, and F. Gao, “Mapless-planner: A robust and fast planning framework for aggressive autonomous flight without map fusion,” in2021 IEEE international conference on robotics and automation (ICRA). IEEE, 2021, pp. 6315–6321

2021
[134]

Alternating minimiza- tion based trajectory generation for quadrotor aggressive flight,

Z. Wang, X. Zhou, C. Xu, J. Chu, and F. Gao, “Alternating minimiza- tion based trajectory generation for quadrotor aggressive flight,”IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4836–4843, 2020

2020
[135]

An efficient b-spline-based kinodynamic replanning framework for quadrotors,

W. Ding, W. Gao, K. Wang, and S. Shen, “An efficient b-spline-based kinodynamic replanning framework for quadrotors,”IEEE Transactions on Robotics, vol. 35, no. 6, pp. 1287–1306, 2019

2019
[136]

Chomp: Covari- ant hamiltonian optimization for motion planning,

M. Zucker, N. Ratliff, A. D. Dragan, M. Pivtoraiko, M. Klingensmith, C. M. Dellin, J. A. Bagnell, and S. S. Srinivasa, “Chomp: Covari- ant hamiltonian optimization for motion planning,”The International journal of robotics research, vol. 32, no. 9-10, pp. 1164–1193, 2013

2013
[137]

Continuous-time trajectory optimization for online uav replanning,

H. Oleynikova, M. Burri, Z. Taylor, J. Nieto, R. Siegwart, and E. Galceran, “Continuous-time trajectory optimization for online uav replanning,” in2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2016, pp. 5332–5339

2016
[138]

Raptor: Robust and perception- aware trajectory replanning for quadrotor fast flight,

B. Zhou, J. Pan, F. Gao, and S. Shen, “Raptor: Robust and perception- aware trajectory replanning for quadrotor fast flight,”IEEE Transac- tions on Robotics, vol. 37, no. 6, pp. 1992–2009, 2021

1992
[139]

Ego-planner: An esdf- free gradient-based local planner for quadrotors,

X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao, “Ego-planner: An esdf- free gradient-based local planner for quadrotors,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 478–485, 2020

2020
[140]

Real-time safe trajectory generation for quadrotor flight in cluttered environments,

J. Chen, K. Su, and S. Shen, “Real-time safe trajectory generation for quadrotor flight in cluttered environments,” in2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2015, pp. 1678–1685

2015
[141]

Faster: Fast and safe trajectory planner for navigation in unknown environments,

J. Tordesillas, B. T. Lopez, M. Everett, and J. P. How, “Faster: Fast and safe trajectory planner for navigation in unknown environments,” IEEE Transactions on Robotics, vol. 38, no. 2, pp. 922–938, 2021

2021
[142]

Flying in dynamic scenes with multitarget velocimetry and perception-enhanced plan- ning,

H. Chen, C.-Y . Wen, F. Gao, and P. Lu, “Flying in dynamic scenes with multitarget velocimetry and perception-enhanced plan- ning,”IEEE/ASME Transactions on Mechatronics, 2023

2023
[143]

Computing large convex regions of obstacle- free space through semidefinite programming,

R. Deits and R. Tedrake, “Computing large convex regions of obstacle- free space through semidefinite programming,” inAlgorithmic Founda- tions of Robotics XI: Selected Contributions of the Eleventh Interna- tional Workshop on the Algorithmic Foundations of Robotics. Springer,...

2015
[144]

Fast iterative region inflation for computing large 2-d/3-d convex regions of obstacle-free space,

Q. Wang, Z. Wang, M. Wang, J. Ji, Z. Han, T. Wu, R. Jin, Y . Gao, C. Xu, and F. Gao, “Fast iterative region inflation for computing large 2-d/3-d convex regions of obstacle-free space,”IEEE Transactions on Robotics, vol. 41, pp. 3223–3243, 2025

2025
[146]

Flight pid controller design for a uav quadrotor

A. Salih, M. Moghavvemi, and H. A. Mohamed, “Flight pid controller design for a uav quadrotor.”Scientific Research and Essays (SRE), vol. 5, no. 23, pp. 3660–3667, 2010

2010
[147]

Dynamic analysis and pid control for a quadrotor,

J. Li and Y . Li, “Dynamic analysis and pid control for a quadrotor,” in 2011 IEEE International Conference on Mechatronics and Automation. IEEE, 2011, pp. 573–578

2011
[148]

PX4 Controller Diagrams,

“PX4 Controller Diagrams,” https://docs.px4.io/main/en/flight stack/ controller diagrams.html, 2024. [Online]. Available: https://docs.px4. io/main/en/flight stack/controller diagrams.html

2024
[149]

Lqr control for a quadrotor using unit quaternions: Mod- eling and simulation,

E. Reyes-Valeria, R. Enriquez-Caldera, S. Camacho-Lara, and J. Guichard, “Lqr control for a quadrotor using unit quaternions: Mod- eling and simulation,” inCONIELECOMP 2013, 23rd International Conference on Electronics, Communications and Computing. IEEE, 2013, pp. 172–178

2013
[150]

Feedback linearization lqr control for quadcopter position tracking,

E. Kuantama, I. Tarca, and R. Tarca, “Feedback linearization lqr control for quadcopter position tracking,” in2018 5th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2018, pp. 204–209

2018
[151]

Performance, precision, and payloads: Adaptive nonlinear mpc for quadrotors,

D. Hanover, P. Foehn, S. Sun, E. Kaufmann, and D. Scaramuzza, “Performance, precision, and payloads: Adaptive nonlinear mpc for quadrotors,”IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 690–697, 2021

2021
[152]

Nonlinear mpc for quadrotor fault-tolerant control,

F. Nan, S. Sun, P. Foehn, and D. Scaramuzza, “Nonlinear mpc for quadrotor fault-tolerant control,”IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5047–5054, 2022

2022
[153]

A comparative study of nonlinear mpc and differential-flatness-based control for quadrotor agile flight,

S. Sun, A. Romero, P. Foehn, E. Kaufmann, and D. Scaramuzza, “A comparative study of nonlinear mpc and differential-flatness-based control for quadrotor agile flight,”IEEE Transactions on Robotics, vol. 38, no. 6, pp. 3357–3373, 2022

2022
[154]

Nonlinear mpc for collision avoidance and control of uavs with dynamic obstacles,

B. Lindqvist, S. S. Mansouri, A.-a. Agha-mohammadi, and G. Niko- lakopoulos, “Nonlinear mpc for collision avoidance and control of uavs with dynamic obstacles,”IEEE robotics and automation letters, vol. 5, no. 4, pp. 6001–6008, 2020

2020
[155]

Predictive control of aerial swarms in cluttered environments,

E. Soria, F. Schiano, and D. Floreano, “Predictive control of aerial swarms in cluttered environments,”Nature Machine Intelligence, vol. 3, no. 6, pp. 545–554, 2021

2021
[156]

Mpc based linear equivalence with control barrier functions for vtol-uavs,

A. M. Ali, H. A. Hashim, and C. Shen, “Mpc based linear equivalence with control barrier functions for vtol-uavs,” in2024 American Control Conference (ACC). IEEE, 2024, pp. 1–6

2024
[157]

A novel null-space-based uav trajectory tracking controller with collision avoidance,

M. C. P. Santos, C. D. Rosales, M. Sarcinelli-Filho, and R. Carelli, “A novel null-space-based uav trajectory tracking controller with collision avoidance,”IEEE/ASME Transactions on Mechatronics, vol. 22, no. 6, pp. 2543–2553, 2017

2017
[158]

Omninxt: A fully open-source and compact aerial robot with omnidirectional visual perception,

P. Liu, C. Feng, Y . Xu, Y . Ning, H. Xu, and S. Shen, “Omninxt: A fully open-source and compact aerial robot with omnidirectional visual perception,” in2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024, pp. 10 605–10 612

2024
[159]

Uniquad: A unified and versatile quadrotor platform series for uav research and application,

Y . Zhang, X. Chen, P. Liu, J. Wang, H. Zou, and S. Shen, “Uniquad: A unified and versatile quadrotor platform series for uav research and application,”arXiv preprint arXiv:2407.00578, 2024

2024 arXiv
[160]

Agilicious: Open- source and open-hardware agile quadrotor for vision-based flight,

P. Foehn, E. Kaufmann, A. Romero, R. Penicka, S. Sun, L. Bauersfeld, T. Laengle, G. Cioffi, Y . Song, A. Loquercio,et al., “Agilicious: Open- source and open-hardware agile quadrotor for vision-based flight,” Science robotics, vol. 7, no. 67, p. eabl6259, 2022

2022
[162]

Aerial gym–isaac gym simulator for aerial robots,

M. Kulkarni, T. J. Forgaard, and K. Alexis, “Aerial gym–isaac gym simulator for aerial robots,”arXiv preprint arXiv:2305.16510, 2023

2023 arXiv
[163]

Marsim: A light-weight point-realistic simulator for lidar- based uavs,

F. Kong, X. Liu, B. Tang, J. Lin, Y . Ren, Y . Cai, F. Zhu, N. Chen, and F. Zhang, “Marsim: A light-weight point-realistic simulator for lidar- based uavs,”IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2954–2961, 2023

2023
[164]

Flightmare: A flexible quadrotor simulator,

Y . Song, S. Naji, E. Kaufmann, A. Loquercio, and D. Scaramuzza, “Flightmare: A flexible quadrotor simulator,” inConference on Robot Learning. PMLR, 2021, pp. 1147–1157

2021
[165]

Airsim drone racing lab,

R. Madaan, N. Gyde, S. Vemprala, M. Brown, K. Nagami, T. Taubner, E. Cristofalo, D. Scaramuzza, M. Schwager, and A. Kapoor, “Airsim drone racing lab,” inNeurIPS 2019 competition and demonstration track. PMLR, 2020, pp. 177–191

2019
[166]

Rotors—a modular gazebo mav simulator framework,

F. Furrer, M. Burri, M. Achtelik, and R. Siegwart, “Rotors—a modular gazebo mav simulator framework,”Robot Operating System (ROS) The Complete Reference (Volume 1), pp. 595–625, 2016

2016
[167]

Heuristic-based incre- mental probabilistic roadmap for efficient uav exploration in dynamic environments,

Z. Xu, C. Suzuki, X. Zhan, and K. Shimada, “Heuristic-based incre- mental probabilistic roadmap for efficient uav exploration in dynamic environments,” in2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 11 832–11 838

2024
[168]

General, single-shot, target-less, and automatic lidar-camera extrinsic calibration toolbox,

K. Koide, S. Oishi, M. Yokozuka, and A. Banno, “General, single-shot, target-less, and automatic lidar-camera extrinsic calibration toolbox,” in2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 11 301–11 307. 16

2023
[169]

Lvi-exc: A target-free lidar- visual-inertial extrinsic calibration framework,

Z. Wang, L. Zhang, Y . Shen, and Y . Zhou, “Lvi-exc: A target-free lidar- visual-inertial extrinsic calibration framework,” inProceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 3319– 3327

2022
[170]

Pixel-level extrinsic self cali- bration of high resolution lidar and camera in targetless environments,

C. Yuan, X. Liu, X. Hong, and F. Zhang, “Pixel-level extrinsic self cali- bration of high resolution lidar and camera in targetless environments,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7517–7524, 2021

2021
[171]

Extending kalibr: Calibrating the extrinsics of multiple imus and of individual axes,

J. Rehder, J. Nikolic, T. Schneider, T. Hinzmann, and R. Siegwart, “Extending kalibr: Calibrating the extrinsics of multiple imus and of individual axes,” in2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 4304–4311

2016
[172]

Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,

T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus, “Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,” in2020 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2020, pp. 5135–5142

2020
[173]

Real-time planning of minimum-time trajectories for agile uav flight,

K. Teissing, M. Novosad, R. Penicka, and M. Saska, “Real-time planning of minimum-time trajectories for agile uav flight,”IEEE Robotics and Automation Letters, vol. 9, no. 11, pp. 10 351–10 358, 2024

2024
[174]

Minimum-time quadrotor waypoint flight in cluttered environments,

R. Penicka and D. Scaramuzza, “Minimum-time quadrotor waypoint flight in cluttered environments,”IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5719–5726, 2022

2022
[175]

Time-optimal planning for quadrotor waypoint flight,

P. Foehn, A. Romero, and D. Scaramuzza, “Time-optimal planning for quadrotor waypoint flight,”Science Robotics, vol. 6, no. 56, p. eabh1221, 2021

2021
[176]

Teach- repeat-replan: A complete and robust system for aggressive flight in complex environments,

F. Gao, L. Wang, B. Zhou, X. Zhou, J. Pan, and S. Shen, “Teach- repeat-replan: A complete and robust system for aggressive flight in complex environments,”IEEE Transactions on Robotics, vol. 36, no. 5, pp. 1526–1545, 2020

2020
[177]

PAMPC: Perception- aware model predictive control for quadrotors,

D. Falanga, P. Foehn, P. Lu, and D. Scaramuzza, “PAMPC: Perception- aware model predictive control for quadrotors,” inIEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS), 2018

2018
[178]

Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories,

M. Faessler, A. Franchi, and D. Scaramuzza, “Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories,”IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 620–626, 2017

2017
[179]

Autonomous drone racing: A survey,

D. Hanover, A. Loquercio, L. Bauersfeld, A. Romero, R. Penicka, Y . Song, G. Cioffi, E. Kaufmann, and D. Scaramuzza, “Autonomous drone racing: A survey,”IEEE Transactions on Robotics, 2024

2024
[180]

Autonomous wind-turbine blade inspection using lidar-equipped un- manned aerial vehicle,

M. Car, L. Markovic, A. Ivanovic, M. Orsag, and S. Bogdan, “Autonomous wind-turbine blade inspection using lidar-equipped un- manned aerial vehicle,”IEEE access, vol. 8, pp. 131 380–131 387, 2020

2020
[181]

Lidar-based automated uav inspec- tion of wind turbine rotor blades,

C. Castelar Wembers, J. Pflughaupt, L. Moshagen, M. Kurenkov, T. Lewejohann, and G. Schildbach, “Lidar-based automated uav inspec- tion of wind turbine rotor blades,”Journal of Field Robotics, vol. 41, no. 4, pp. 1116–1132, 2024

2024
[182]

High level framework for bridge inspection using lidar-equipped uav,

N. Bolourian, M. M. Soltani, A. Albahria, and A. Hammad, “High level framework for bridge inspection using lidar-equipped uav,” in ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, vol. 34. IAARC Publications, 2017

2017
[183]

Lidar-equipped uav path planning considering potential locations of defects for bridge inspection,

N. Bolourian and A. Hammad, “Lidar-equipped uav path planning considering potential locations of defects for bridge inspection,”Au- tomation in Construction, vol. 117, p. 103250, 2020

2020
[184]

Uav-lidar aids automatic intelligent powerline inspection,

H. Guan, X. Sun, Y . Su, T. Hu, H. Wang, H. Wang, C. Peng, and Q. Guo, “Uav-lidar aids automatic intelligent powerline inspection,” International Journal of Electrical Power & Energy Systems, vol. 130, p. 106987, 2021

2021
[185]

Au- tonomous reactive lidar-based mapping for powerline inspection,

J. Paneque, V . Valseca, J. Mart ´ınez-de Dios, and A. Ollero, “Au- tonomous reactive lidar-based mapping for powerline inspection,” in2022 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2022, pp. 962–971

2022
[186]

Search and rescue under the forest canopy using multiple uavs,

Y . Tian, K. Liu, K. Ok, L. Tran, D. Allen, N. Roy, and J. P. How, “Search and rescue under the forest canopy using multiple uavs,”The International Journal of Robotics Research, vol. 39, no. 10-11, pp. 1201–1221, 2020

2020
[187]

Evaluation of secondary landslide susceptibility for the rescue activity using lidar uav data,

S. Doshida, “Evaluation of secondary landslide susceptibility for the rescue activity using lidar uav data,”Understanding and Reducing Landslide Disaster Risk: Volume 2 From Mapping to Hazard and Risk Zonation 5th, pp. 283–287, 2021

2021
[188]

Visual inertial lidar odometry and mapping (vi-loam) fusion for uav-based parcel delivery,

D. Dissanayaka, “Visual inertial lidar odometry and mapping (vi-loam) fusion for uav-based parcel delivery,” Ph.D. dissertation, Memorial University of Newfoundland, 2022

2022
[189]

A hierarchical unsupervised method for power line classification from airborne lidar data,

Y . Wang, Q. Chen, L. Liu, and K. Li, “A hierarchical unsupervised method for power line classification from airborne lidar data,”Interna- tional journal of digital earth, vol. 12, no. 12, pp. 1406–1422, 2019

2019
[190]

Lidar applications in precision agriculture for cultivating crops: A review of recent advances,

G. Rivera, R. Porras, R. Florencia, and J. P. S ´anchez-Sol´ıs, “Lidar applications in precision agriculture for cultivating crops: A review of recent advances,”Computers and electronics in agriculture, vol. 207, p. 107737, 2023

2023
[191]

Estimation of sugarcane yield using a machine learning approach based on uav-lidar data,

J.-X. Xu, J. Ma, Y .-N. Tang, W.-X. Wu, J.-H. Shao, W.-B. Wu, S.-Y . Wei, Y .-F. Liu, Y .-C. Wang, and H.-Q. Guo, “Estimation of sugarcane yield using a machine learning approach based on uav-lidar data,” Remote Sensing, vol. 12, no. 17, p. 2823, 2020

2020
[192]

Geltip: A finger-shaped optical tactile sensor for robotic manipulation,

D. F. Gomes, Z. Lin, and S. Luo, “Geltip: A finger-shaped optical tactile sensor for robotic manipulation,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020, pp. 9903–9909

2020
[193]

Monitoring the effects of weed management strategies on tree canopy structure and growth using uav-lidar in a young almond orchard,

T. Caras, R. N. Lati, D. Holland, V . M. Dubinin, K. Hatib, I. Shulner, O. Keiesar, G. Liddor, and T. Paz-Kagan, “Monitoring the effects of weed management strategies on tree canopy structure and growth using uav-lidar in a young almond orchard,”Computers and Electronics in Ag...

2024
[194]

Analysis of cotton height spatial variability based on uav-lidar,

K. Liu, X. Dong, and B. Qiu, “Analysis of cotton height spatial variability based on uav-lidar,”International Journal of Precision Agricultural Aviation, vol. 3, no. 3, 2020

2020
[195]

High-throughput phenotyping of canopy height in cool-season crops using sensing techniques,

C. Zhang, W. A. Craine, R. J. McGee, G. J. Vandemark, J. B. Davis, J. Brown, S. H. Hulbert, and S. Sankaran, “High-throughput phenotyping of canopy height in cool-season crops using sensing techniques,”Agronomy Journal, vol. 113, no. 4, pp. 3269–3280, 2021

2021
[196]

Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images ob- tained from ground-based lidar,

K. Itakura and F. Hosoi, “Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images ob- tained from ground-based lidar,”Journal of Agricultural Meteorology, vol. 74, no. 3, pp. 109–113, 2018

2018
[197]

On-ground vineyard reconstruction using a lidar-based automated system,

H. Moreno, C. Valero, J. M. Bengochea-Guevara, ´A. Ribeiro, M. Garrido-Izard, and D. And ´ujar, “On-ground vineyard reconstruction using a lidar-based automated system,”Sensors, vol. 20, no. 4, p. 1102, 2020

2020
[199]

A controllable flying vehicle with a single moving part,

W. Zhang, M. W. Mueller, and R. D’Andrea, “A controllable flying vehicle with a single moving part,” in2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 3275–3281

2016
[200]

Design and control of the first foldable single-actuator rotary wing micro aerial vehicle,

S. K. H. Win, L. S. T. Win, D. Sufiyan, and S. Foong, “Design and control of the first foldable single-actuator rotary wing micro aerial vehicle,”Bioinspiration & biomimetics, vol. 16, no. 6, p. 066019, 2021

2021
[201]

A bioinspired revolving-wing drone with passive attitude stability and efficient hovering flight,

S. Bai, Q. He, and P. Chirarattananon, “A bioinspired revolving-wing drone with passive attitude stability and efficient hovering flight,” Science Robotics, vol. 7, no. 66, p. eabg5913, 2022

2022
[208]

Swarm of micro flying robots in the wild,

X. Zhou, X. Wen, Z. Wang, Y . Gao, H. Li, Q. Wang, T. Yang, H. Lu, Y . Cao, C. Xu,et al., “Swarm of micro flying robots in the wild,” Science Robotics, vol. 7, no. 66, p. eabm5954, 2022

2022
[209]

Active perception based formation control for multiple aerial vehicles,

R. Tallamraju, E. Price, R. Ludwig, K. Karlapalem, H. H. B ¨ulthoff, M. J. Black, and A. Ahmad, “Active perception based formation control for multiple aerial vehicles,”IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4491–4498, 2019

2019
[210]

Do you see what i see? coordinating multiple aerial cameras for robot cinematography,

A. Bucker, R. Bonatti, and S. Scherer, “Do you see what i see? coordinating multiple aerial cameras for robot cinematography,” in 17 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 7972–7979

2021
[211]

3d human reconstruction in the wild with collaborative aerial cameras,

C. Ho, A. Jong, H. Freeman, R. Rao, R. Bonatti, and S. Scherer, “3d human reconstruction in the wild with collaborative aerial cameras,” in2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021, pp. 5263–5269

2021
[212]

Autonomous aerial cine- matography in unstructured environments with learned artistic decision- making,

R. Bonatti, W. Wang, C. Ho, A. Ahuja, M. Gschwindt, E. Camci, E. Kayacan, S. Choudhury, and S. Scherer, “Autonomous aerial cine- matography in unstructured environments with learned artistic decision- making,”Journal of Field Robotics, vol. 37, no. 4, pp. 606–641, 2020

2020
[213]

Racer: Rapid collaborative explo- ration with a decentralized multi-uav system,

B. Zhou, H. Xu, and S. Shen, “Racer: Rapid collaborative explo- ration with a decentralized multi-uav system,”IEEE Transactions on Robotics, vol. 39, no. 3, pp. 1816–1835, 2023

2023
[214]

Fast and communication-efficient multi-uav exploration via voronoi parti- tion on dynamic topological graph,

Q. Dong, H. Xi, S. Zhang, Q. Bi, T. Li, Z. Wang, and X. Zhang, “Fast and communication-efficient multi-uav exploration via voronoi parti- tion on dynamic topological graph,” in2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024, pp. 14 0...

2024
[215]

Smmr-explore: Submap-based multi-robot exploration system with multi-robot multi-target potential field exploration method,

J. Yu, J. Tong, Y . Xu, Z. Xu, H. Dong, T. Yang, and Y . Wang, “Smmr-explore: Submap-based multi-robot exploration system with multi-robot multi-target potential field exploration method,” in2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. ...

2021
[216]

On solving mirror reflection in lidar sensing,

S.-W. Yang and C.-C. Wang, “On solving mirror reflection in lidar sensing,”IEEE/ASME Transactions on Mechatronics, vol. 16, no. 2, pp. 255–265, 2010

2010
[217]

Mems mirrors for lidar: A review,

D. Wang, C. Watkins, and H. Xie, “Mems mirrors for lidar: A review,” Micromachines, vol. 11, no. 5, p. 456, 2020

2020
[218]

Metasurface-enhanced light detection and ranging technology,

R. Juliano Martins, E. Marinov, M. A. B. Youssef, C. Kyrou, M. Jou- bert, C. Colmagro, V . Gˆat´e, C. Turbil, P.-M. Coulon, D. Turover,et al., “Metasurface-enhanced light detection and ranging technology,”Nature Communications, vol. 13, no. 1, p. 5724, 2022

2022
[219]

Autonomous door opening with the interacting-boomcopter unmanned aerial vehicle,

D. McArthur, A. Chowdhury, and D. Cappelleri, “Autonomous door opening with the interacting-boomcopter unmanned aerial vehicle,” Journal of Mechanisms and Robotics, vol. 12, pp. 1–10, 12 2019

2019
[220]

Learning high-speed flight in the wild,

A. Loquercio, E. Kaufmann, R. Ranftl, M. M ¨uller, V . Koltun, and D. Scaramuzza, “Learning high-speed flight in the wild,”Science Robotics, vol. 6, no. 59, p. eabg5810, 2021. Yunfan Renreceived his B.Eng. degree in Automa- tion from Harbin Institute of Technology, Shenzhen, C...

2021

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [37]

3-d site mapping with the cmu autonomous helicopter,

M. Ryan, “3-d site mapping with the cmu autonomous helicopter,” in The 5th International Conference on Intelligent Autonomous Systems, 1998, 1998

1998

[2] [205]

Autonomous tail-sitter flights in unknown environments,

G. Lu, Y . Ren, F. Zhu, H. Li, R. Xue, Y . Cai, X. Lyu, and F. Zhang, “Autonomous tail-sitter flights in unknown environments,”IEEE Trans- actions on Robotics, vol. 41, pp. 1098–1117, 2025

2025

[3] [161]

A self-rotating, single-actuated uav with extended sensor field of view for autonomous navigation,

N. Chen, F. Kong, W. Xu, Y . Cai, H. Li, D. He, Y . Qin, and F. Zhang, “A self-rotating, single-actuated uav with extended sensor field of view for autonomous navigation,”Science Robotics, vol. 8, no. 76, p. eade4538, 2023

2023

[4] [198]

Piccolissimo: The smallest micro aerial vehicle,

M. Piccoli and M. Yim, “Piccolissimo: The smallest micro aerial vehicle,” in2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017, pp. 3328–3333

2017

[5] [202]

Splitflyer air: A modular quadcopter that disassembles into two bicopters mid-air,

S. Bai and P. Chirarattananon, “Splitflyer air: A modular quadcopter that disassembles into two bicopters mid-air,”IEEE/ASME Transac- tions on Mechatronics, vol. 27, no. 6, pp. 4729–4740, 2022

2022

[6] [203]

Lockheed martin’s samarai nano air vehicle: challenges, research, and realization,

S. Jameson, K. Fregene, M. Chang, N. Allen, H. Youngren, and J. Scroggins, “Lockheed martin’s samarai nano air vehicle: challenges, research, and realization,” in50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 2012, p. 584

2012

[7] [204]

Autonomous quadrotor flight despite rotor failure with onboard vision sensors: Frames vs. events,

S. Sun, G. Cioffi, C. De Visser, and D. Scaramuzza, “Autonomous quadrotor flight despite rotor failure with onboard vision sensors: Frames vs. events,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 580–587, 2021

2021

[8] [206]

Swashplateless-elevon actuation for a dual-rotor tail-sitter vtol uav,

N. Chen, F. Kong, H. Li, J. Liu, Z. Ye, W. Xu, F. Zhu, X. Lyu, and F. Zhang, “Swashplateless-elevon actuation for a dual-rotor tail-sitter vtol uav,” in2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023, pp. 6970–6976

2023

[9] [207]

Trajectory generation and tracking control for aggressive tail-sitter flights,

G. Lu, Y . Cai, N. Chen, F. Kong, Y . Ren, and F. Zhang, “Trajectory generation and tracking control for aggressive tail-sitter flights,”The International Journal of Robotics Research, vol. 43, no. 3, pp. 241–280, 2024

2024

[10] [22]

Scan alignment and 3-d surface modeling with a helicopter platform,

S. Thrun, M. Diel, and D. H ¨ahnel, “Scan alignment and 3-d surface modeling with a helicopter platform,” inField and Service Robotics: Recent Advances in Reserch and Applications. Springer, 2006, pp. 287–297

2006

[11] [145]

Integrated planning and control for quadrotor navigation in presence of suddenly appearing objects and disturbances,

W. Liu, Y . Ren, and F. Zhang, “Integrated planning and control for quadrotor navigation in presence of suddenly appearing objects and disturbances,”IEEE Robotics and Automation Letters, pp. 1–8, 2023

2023

[12] [48]

Rog-map: An efficient robocentric occupancy grid map for large-scene and high-resolution lidar-based motion planning,

Y . Ren, Y . Cai, F. Zhu, S. Liang, and F. Zhang, “Rog-map: An efficient robocentric occupancy grid map for large-scene and high-resolution lidar-based motion planning,” in2024 IEEE/RSJ International Con- ference on Intelligent Robots and Systems (IROS). IEEE, 2024, pp. 8119–8125

2024

[13] [49]

Occupancy grid mapping without ray-casting for high-resolution lidar sensors,

Y . Cai, F. Kong, Y . Ren, F. Zhu, J. Lin, and F. Zhang, “Occupancy grid mapping without ray-casting for high-resolution lidar sensors,”IEEE Transactions on Robotics, 2023

2023

[14] [62]

Swarm-lio2: Decentralized, efficient lidar-inertial odometry for uav swarms,

F. Zhu, Y . Ren, L. Yin, F. Kong, Q. Liu, R. Xue, W. Liu, Y . Cai, G. Lu, H. Li,et al., “Swarm-lio2: Decentralized, efficient lidar-inertial odometry for uav swarms,”IEEE Transactions on Robotics, vol. 41, pp. 960–981, 2024

2024