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arxiv: 2603.22508 · v2 · pith:4T5SX2HTnew · submitted 2026-03-23 · 💻 cs.RO · cs.SY· eess.SY

Parallel OctoMapping: A Scalable Framework for Enhanced Path Planning in Autonomous Navigation

Yihui Mao , Tian Tan , Xuehui Shen , Warren E. Dixon , Rushikesh Kamalapurkar This is my paper

Pith reviewed 2026-05-22 10:23 UTC · model grok-4.3

classification 💻 cs.RO cs.SYeess.SY
keywords Parallel OctoMappingPOMPOccupancy mappingPath planningFree space refinementAutonomous navigationOctoMapMulti-threaded computation
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The pith

Parallel OctoMapping refines free space in fixed-resolution occupancy grids via parallel computation to improve path planning success and length.

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

Fixed-resolution mapping often produces conservative obstacle shapes that block good paths or cause planners to fail in cluttered scenes. The paper introduces Parallel OctoMapping, or POMP, which uses multi-threaded processing on an OctoMap structure to expand the usable free space at the same grid resolution. This refinement keeps the map accurate and compatible with ordinary search-based planners. As a result, robots find paths more reliably and those paths tend to be shorter, while the mapping step itself runs faster. The technique slots directly into existing navigation stacks without requiring changes to the planner.

Core claim

To the best of our knowledge, POMP is the first method that, at a fixed occupancy-grid resolution, refines the representation of free space while preserving map fidelity and compatibility with existing search-based planners, yielding higher pathfinding success rates and shorter path lengths especially in cluttered environments while substantially improving computational efficiency.

What carries the argument

Parallel OctoMapping (POMP), an OctoMap-based technique that applies multi-threaded computation to maximize available free space at fixed resolution.

If this is right

  • Existing path planners can use POMP maps without any code changes and still see higher success rates.
  • Paths become shorter in cluttered spaces because more free space is recognized at the same resolution.
  • Mapping computation speeds up through multi-threading, supporting larger environments.
  • Map fidelity stays intact, so safety guarantees from occupancy grids remain valid.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same parallel refinement idea could be tested on other voxel or grid representations beyond OctoMap.
  • In dynamic scenes the faster update rate might allow planners to react to moving obstacles with less delay.
  • Hardware with many cores would show the largest efficiency gains, suggesting a natural fit for onboard robot computers.

Load-bearing premise

Refinements to free-space representation at fixed resolution can be achieved through parallel computation without introducing map inaccuracies or breaking compatibility with standard search-based planners.

What would settle it

A side-by-side test in a cluttered 3-D environment at identical grid resolution that measures whether POMP produces measurably higher success rates or shorter paths than standard OctoMap while keeping the same number of occupied cells.

Figures

Figures reproduced from arXiv: 2603.22508 by Rushikesh Kamalapurkar, Tian Tan, Warren E. Dixon, Xuehui Shen, Yihui Mao.

Figure 1
Figure 1. Figure 1: System overview of our proposed mapping framework [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Parallel insertion of point clouds into the Octree with concurrent node [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the threshold setup and region classification: [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the mapping configuration from Octree leaf nodes (left) to leaf regions and their projection onto the OGM (right). The occupancy grid [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of the diagonal examination pairs and rules for determining [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Illustration of the OGM produced by our method. This panel shows the bottom-right corner of the map in Fig. 4. For clarity, the OGM cell boundaries [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 2
Figure 2. Figure 2: In the 3D case, the eight regions of a leaf node yield [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 7
Figure 7. Figure 7: Top row (POMP): Using the obstacle setup in Fig. 4 and the mapping rule in Fig. 5, POMP assigns each region an [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Paths planned using POMP (red) and direct (traditional) OGM (blue) superimposed on the point cloud (a) and on the occupancy grid (b). The orange [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Experiment A-1: Runtime comparison with point clouds, showing from top to bottom: 500,000, 1,000,000, and 2,000,000 points. 2) OctoMap Construction & Occupancy Mapping: The second experiment evaluates the total runtime of OctoMap construction and the subsequent projection of region safe states from leaf nodes onto OGM cell occupancy, and com￾pares it with two baselines: a direct OGM method and the origina… view at source ↗
Figure 9
Figure 9. Figure 9: Two maps utilized in the randomized point clouds experiments. [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Experiment A-2: Comparison of the total runtime of OctoMap [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: Experiment A-4 : Planning runtime and path length are reported only [PITH_FULL_IMAGE:figures/full_fig_p010_13.png] view at source ↗
Figure 12
Figure 12. Figure 12: Experiment A-3: Comparison of the navigable space ratio (NSR) [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: Benchmark maps used in the application experiments include [PITH_FULL_IMAGE:figures/full_fig_p011_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Cloud_7 from WSF-19 in the Treescope dataset. The upper-left panel shows the raw point cloud map, the lower-left panel shows the octree constructed from the point cloud, and the right panel shows the planning result. The blue path is generated by POMP, while the purple path is obtained using the direct OGM. As shown in Table VIII, POMP shows a clear advantage in the success rate. Compared with the baselin… view at source ↗
Figure 16
Figure 16. Figure 16: Visualization of the input point cloud VAT-0723U-03 and the resulting POMP Octree, from left to right with leaf sizes [PITH_FULL_IMAGE:figures/full_fig_p014_16.png] view at source ↗
read the original abstract

Mapping is essential in robotics and autonomous systems because it provides the spatial foundation for path planning. Efficient mapping enables planning algorithms to generate reliable paths while ensuring safety and adapting in real time to complex environments. Fixed-resolution mapping methods often produce overly conservative obstacle representations that lead to suboptimal paths or planning failures in cluttered scenes. To address this issue, we introduce Parallel OctoMapping (POMP), an efficient OctoMap-based mapping technique that maximizes available free space and supports multi-threaded computation. To the best of our knowledge, POMP is the first method that, at a fixed occupancy-grid resolution, refines the representation of free space while preserving map fidelity and compatibility with existing search-based planners. It can therefore be integrated into existing planning pipelines, yielding higher pathfinding success rates and shorter path lengths, especially in cluttered environments, while substantially improving computational efficiency.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces Parallel OctoMapping (POMP), a multi-threaded extension of OctoMap that refines free-space representation at fixed grid resolution through parallel ray-casting, node updates, and probability fusion. It claims this yields maps that remain faithful to sequential OctoMap semantics, remain compatible with unmodified search-based planners, and produce higher pathfinding success rates plus shorter paths (especially in cluttered scenes) while improving computational efficiency.

Significance. If the fidelity-preservation and compatibility claims hold with quantitative backing, the work would offer a practical, drop-in improvement for existing OctoMap-based navigation stacks, addressing a common tension between map resolution, free-space availability, and planner performance without requiring planner modifications or higher memory use.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (method): the central claim that parallel updates 'preserve map fidelity' and produce 'identical' free-space boundaries to sequential OctoMap is load-bearing for the entire contribution, yet the provided text supplies no explicit equivalence proof, occupancy-probability difference metric, or free-voxel count comparison between parallel and sequential runs on identical sensor data. Without such verification, improved planner success could be an artifact of altered update order or clamping rather than a true refinement.
  2. [§4] §4 (experiments): no quantitative results, success-rate tables, path-length statistics, timing benchmarks, or error-analysis protocol appear in the supplied information. The abstract asserts 'higher pathfinding success rates and shorter path lengths' but the absence of these data prevents assessment of effect size or statistical significance.
minor comments (2)
  1. [§3.2] Notation for parallel fusion step is introduced without a clear equation relating thread-local occupancy updates to the final log-odds value; a single equation would improve reproducibility.
  2. [Figures] Figure captions should explicitly state whether the visualized maps were generated with the parallel or sequential implementation to allow direct visual comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. The two major comments identify areas where additional evidence and detail would strengthen the manuscript. We address each point below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (method): the central claim that parallel updates 'preserve map fidelity' and produce 'identical' free-space boundaries to sequential OctoMap is load-bearing for the entire contribution, yet the provided text supplies no explicit equivalence proof, occupancy-probability difference metric, or free-voxel count comparison between parallel and sequential runs on identical sensor data. Without such verification, improved planner success could be an artifact of altered update order or clamping rather than a true refinement.

    Authors: We agree that an explicit quantitative verification of fidelity is essential to support the core claim. Although the parallel implementation follows the identical Bayesian update equations, ray-casting logic, and clamping thresholds as sequential OctoMap, with mutex-protected node updates to preserve update order semantics, the manuscript currently relies on this design argument without empirical confirmation. In the revised version we will insert a dedicated subsection in §3 that reports, on identical sensor streams from the same datasets used in the experiments, the maximum and mean absolute occupancy-probability difference, the count of voxels whose free/occupied label differs, and the boundary voxel discrepancy. These metrics will demonstrate that any divergence remains within floating-point tolerance and does not alter free-space topology. revision: yes

  2. Referee: [§4] §4 (experiments): no quantitative results, success-rate tables, path-length statistics, timing benchmarks, or error-analysis protocol appear in the supplied information. The abstract asserts 'higher pathfinding success rates and shorter path lengths' but the absence of these data prevents assessment of effect size or statistical significance.

    Authors: The full manuscript contains experimental results in §4, yet we acknowledge that the presentation may not have been sufficiently detailed or statistically rigorous in the review copy. We will expand §4 to include (i) success-rate tables across multiple environments and planners, (ii) mean and standard-deviation path lengths, (iii) wall-clock timing for mapping and subsequent planning, and (iv) an explicit error-analysis protocol that compares generated maps against ground-truth occupancy grids. Effect sizes and, where sample sizes permit, statistical significance tests will be reported to allow readers to evaluate the magnitude and reliability of the observed improvements. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents Parallel OctoMapping (POMP) as an algorithmic technique for parallel OctoMap updates to refine free-space representation at fixed resolution. The central claims concern computational efficiency, planner compatibility, and fidelity preservation through multi-threaded ray-casting and node updates. These are described as properties of the proposed implementation rather than mathematical derivations or predictions that reduce to fitted inputs or self-citations by construction. No equations, self-definitional loops, or load-bearing self-citations appear in the abstract or context that would force the result to equal its inputs. The work is self-contained as a technical contribution whose fidelity claims are subject to experimental verification against sequential baselines.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; no explicit free parameters, new entities, or detailed axioms are stated. The approach rests on standard robotics assumptions about OctoMap validity and planner compatibility.

axioms (1)
  • domain assumption OctoMap provides a valid and extensible spatial representation for path planning
    The method builds directly on OctoMap as the base structure for occupancy mapping.

pith-pipeline@v0.9.0 · 5694 in / 1287 out tokens · 54661 ms · 2026-05-22T10:23:21.231524+00:00 · methodology

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    In 3D, the lower 8 bits record the safe / unsafe flags of the eight regions, and the upper 8 bits record the corresponding clear / non-clear flags... four diagonal pairs, labeled[1,8],[2,7],[3,6],[4,5]

What do these tags mean?
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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Decentralized Scalar Field Mapping using Gaussian Process

    eess.SY 2026-04 unverdicted novelty 7.0

    A novel decentralized intersection data-sharing and assimilation protocol for multi-agent Gaussian processes exploits posterior discrepancies to improve team-level predictive performance while preserving locality.

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

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

    A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “Octomap: An efficient probabilistic 3D mapping framework based on octrees,”Auton. Robots, vol. 34, pp. 189–206, 2013

    work page 2013

  2. [2]

    Kinectfusion: Real-time dense surface mapping and tracking,

    R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon, “Kinectfusion: Real-time dense surface mapping and tracking,” inProc. IEEE Int. Symp. Mixed Augmented Reality, 2011, pp. 127–136

    work page 2011

  3. [3]

    Real-time 3D reconstruction in dynamic scenes using point-based fusion,

    M. Keller, D. Lefloch, M. Lambers, S. Izadi, T. Weyrich, and A. Kolb, “Real-time 3D reconstruction in dynamic scenes using point-based fusion,” inProc. Int. Conf. 3D Vision (3DV), 2013, pp. 1–8

    work page 2013

  4. [4]

    Scalar field mapping with adaptive high-intensity region avoidance,

    M. Qureshi, T. E. Ogri, Z. I. Bell, and R. Kamalapurkar, “Scalar field mapping with adaptive high-intensity region avoidance,” inProc. IEEE Conf. Control Technol. Appl., Newcastle upon Tyne, UK, Aug. 2024, pp. 388–393. [Online]. Available: https: //ieeexplore.ieee.org/document/10666509

    work page arXiv 2024

  5. [5]

    Probabilistic robotics,

    S. Thrun, “Probabilistic robotics,”Communications of the ACM, vol. 45, no. 3, pp. 52–57, 2002

    work page 2002

  6. [6]

    A note on two problems in connexion with graphs,

    E. W. Dijkstra, “A note on two problems in connexion with graphs,” in Edsger Wybe Dijkstra: his life, work, and legacy, 2022, pp. 287–290

    work page 2022

  7. [7]

    A formal basis for the heuristic determination of minimum cost paths,

    P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,”IEEE Trans. Syst. Sci. Cybern., vol. 4, no. 2, pp. 100–107, 1968

    work page 1968

  8. [8]

    Optimal and efficient path planning for partially-known environments,

    A. Stentz, “Optimal and efficient path planning for partially-known environments,” inProc. IEEE Int. Conf. Robot. Autom., 1994, pp. 3310– 3317

    work page 1994

  9. [9]

    Online graph pruning for pathfinding on grid maps,

    D. Harabor and A. Grastien, “Online graph pruning for pathfinding on grid maps,” inProc. AAAI Conf. Artif. Intell., vol. 25, no. 1, 2011, pp. 1114–1119

    work page 2011

  10. [10]

    Latombe,Robot Motion Planning

    J.-C. Latombe,Robot Motion Planning. Boston, MA: Kluwer Academic Publishers, 1991

    work page 1991

  11. [11]

    Data frame aware optimized octomap-based dynamic object detection and removal in mobile laser scanning data,

    Z. Liu, P. van Oosterom, J. Balado, A. Swart, and B. Beers, “Data frame aware optimized octomap-based dynamic object detection and removal in mobile laser scanning data,”Alexandria Engineering Journal, vol. 74, pp. 327–344, 2023

    work page 2023

  12. [12]

    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 Robot. Autom. Lett., vol. 5, no. 4, pp. 6411–6418, 2020

    work page 2020

  13. [13]

    Recurrent- OctoMap: Learning state-based map refinement for long-term semantic mapping with 3-d lidar data,

    L. Sun, Z. Yan, A. Zaganidis, C. Zhao, and T. Duckett, “Recurrent- OctoMap: Learning state-based map refinement for long-term semantic mapping with 3-d lidar data,”IEEE Robot. Autom. Lett., vol. 3, pp. 3749–3756, 2018

    work page 2018

  14. [14]

    OctoCache: Caching voxels for accelerating 3D occupancy mapping in autonomous systems,

    P. Chen, M. Li, Z. Wan, Y .-S. Hsiao, M. Yu, V . J. Reddi, and Z. Liu, “OctoCache: Caching voxels for accelerating 3D occupancy mapping in autonomous systems,” inProc. ACM Int. Conf. Archit. Support Program. Lang. Oper. Syst.ACM, 2025, pp. 704–718. [Online]. Available: https://doi.org/10.1145/3676641.3716263

    work page doi:10.1145/3676641.3716263 2025

  15. [15]

    Super rays and culling region for real-time updates on grid-based occupancy maps,

    Y . Kwon, D. Kim, I. An, and S.-e. Yoon, “Super rays and culling region for real-time updates on grid-based occupancy maps,”IEEE Trans. Robot., vol. 35, no. 2, pp. 482–497, 2019

    work page 2019

  16. [16]

    OctoMap-RT: Fast probabilistic volumetric mapping using ray-tracing GPUs,

    H. Min, K. M. Han, and Y . J. Kim, “OctoMap-RT: Fast probabilistic volumetric mapping using ray-tracing GPUs,”IEEE Robot. Autom. Lett., vol. 8, no. 9, pp. 5696–5703, 2023

    work page 2023

  17. [17]

    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 Trans. Robot., vol. 40, pp. 172–192, 2024

    work page 2024

  18. [18]

    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,” inProc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), 2017, pp. 1366–1373

    work page 2017

  19. [19]

    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,” inProc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2019, pp. 4423–4430. [Online]. Available: https://ieeexplore.ieee.org/document/8968199

    work page arXiv 2019

  20. [20]

    V oxfield: Non-projective signed distance fields for online planning and 3D reconstruction,

    Y . Pan, Y . Kompis, L. Bartolomei, R. Mascaro, C. Stachniss, and M. Chli, “V oxfield: Non-projective signed distance fields for online planning and 3D reconstruction,” inProc. IEEE/RSJ Int. Conf. Intell. Robots Syst.IEEE, 2022, pp. 5331–5338. [Online]. Available: https://ieeexplore.ieee.org/document/9981318

    work page arXiv 2022

  21. [21]

    A volumetric method for building complex models from range images,

    B. Curless and M. Levoy, “A volumetric method for building complex models from range images,” inProc. ACM SIGGRAPH, 1996, pp. 303– 312

    work page 1996

  22. [22]

    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 Robot. Autom. Lett., vol. 2, no. 3, pp. 1688–1695, 2017

    work page 2017

  23. [23]

    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 Robot. Autom. Lett., vol. 6, no. 2, pp. 478–485, 2020

    work page 2020

  24. [24]

    Safety-critical planning and control for dynamic obstacle avoidance using control barrier functions,

    S. Liu, Y . Mao, and C. A. Belta, “Safety-critical planning and control for dynamic obstacle avoidance using control barrier functions,” inProc. Am. Control Conf. (ACC), Denver, CO, USA, 2025, pp. 348–354

    work page 2025

  25. [25]

    3DVFH+: Real-time three- dimensional obstacle avoidance using an Octomap,

    S. Vanneste, B. Bellekens, and M. Weyn, “3DVFH+: Real-time three- dimensional obstacle avoidance using an Octomap,” inProc. Int. Work- shop on Model-Driven Robot Software Engineering, no. 1319, 2014, pp. 91–102

    work page 2014

  26. [26]

    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, vol. 32, no. 6, pp. 1–11, 2013

    work page 2013

  27. [27]

    Reinders,Intel threading building blocks: outfitting C++ for multi- core processor parallelism

    J. Reinders,Intel threading building blocks: outfitting C++ for multi- core processor parallelism. O’Reilly Media, Inc., 2007

    work page 2007

  28. [28]

    Using occupancy grids for mobile robot perception and navigation,

    A. Elfes, “Using occupancy grids for mobile robot perception and navigation,”Computer, vol. 22, no. 6, pp. 46–57, 1989

    work page 1989

  29. [29]

    High resolution maps from wide angle sonar,

    H. Moravec and A. Elfes, “High resolution maps from wide angle sonar,” inProc. IEEE Int. Conf. Robot. Autom., vol. 2, 1985, pp. 116–121

    work page 1985

  30. [30]

    SensatUrban: Learning semantics from urban-scale photogrammetric point clouds,

    Q. Hu, B. Yang, S. Khalid, W. Xiao, N. Trigoni, and A. Markham, “SensatUrban: Learning semantics from urban-scale photogrammetric point clouds,”Int. J. Comput. Vis., vol. 130, no. 2, pp. 316–343, 2022

    work page 2022

  31. [31]

    The Replica Dataset: A Digital Replica of Indoor Spaces

    J. Straub, T. Whelan, L. Ma, Y . Chen, E. Wijmans, S. Green, J. J. Engel, R. Mur-Artal, C. Ren, S. Verma, A. Clarkson, M. Yan, B. Budge, Y . Yan, X. Pan, J. Yon, Y . Zou, K. Leon, N. Carter, J. Briales, T. Gillingham, E. Mueggler, L. Pesqueira, M. Savva, D. Batra, H. M. Strasdat, R. De Nardi, M. Goesele, S. Lovegrove, and R. Newcombe, “The Replica dataset...

    work page internal anchor Pith review Pith/arXiv arXiv 1906

  32. [32]

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

    D. Cheng, F. C. Ojeda, 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 orchards,” arXiv:2310.02162, 2023

    work page arXiv 2023

  33. [33]

    FAST-LIO: A fast, robust LiDAR-Inertial odom- etry package by tightly-coupled iterated Kalman filter,

    W. Xu and F. Zhang, “FAST-LIO: A fast, robust LiDAR-Inertial odom- etry package by tightly-coupled iterated Kalman filter,”IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 3317–3324, 2021

    work page 2021