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HERCULES: An Open-Source Simulation Framework for Heterogeneous Multi-Robot SLAM, Collaborative Perception, and Exploration

T0 review · 0 major / 2 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read HERCULES resolves prior simulator limitations to enable concurrent UAV and UGV operations in large-scale photorealistic dynamic environments.

desk verdict HERCULES is a solid tool release that adds heterogeneous UAV-UGV support, LWIR sensors, and a released multi-robot benchmark to AirSim. read the letter →

arxiv 2606.22756 v1 pith:2U35FM4M submitted 2026-06-22 cs.RO cs.CVcs.MAcs.SYeess.SY

Sandilya Sai Garimella , Daniel Chase Butterfield , Sean Wilson , Lu Gan This is my paper
classification cs.ROcs.CVcs.MAcs.SYeess.SY
keywords simulationframeworkheterogeneousmulti-robotUAVUGVSLAMcollaborativeperceptionopen-sourceexplorationROS2
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

The paper introduces HERCULES as an open-source simulation framework and data-collection pipeline for heterogeneous multi-robot autonomy. It builds on Unreal Engine 5 simulators to support simultaneous aerial and ground vehicle operations in kilometer-scale settings that include dynamic agents and phenomena. Additions include a waypoint-tracking controller for ground vehicles that matches aerial control interfaces and a shared navigation stack for mapping, traversability analysis, planning, and control across platforms. The system expands sensors with long-wave infrared cameras and night-vision modes, supplies ROS 2 wrappers with time synchronization, and runs in both passive replay and active closed-loop modes. Experiments demonstrate its use for multi-robot SLAM, collaborative perception, and exploration, accompanied by public release of code, documentation, and benchmark datasets.

What carries the argument

The waypoint-tracking UGV controller that mirrors UAV control interfaces together with the shared navigation stack for mapping, traversability analysis, planning, and control across heterogeneous platforms.

What would settle it

A direct comparison in which the shared navigation stack produces traversability maps or planned paths that function for one platform type but not the other, or in which time synchronization breaks during concurrent UAV-UGV runs, would falsify the central claim.

Watch

Extended reading notes

Core claim

HERCULES is an open-source simulator and data-collection pipeline that resolves key architectural limitations of prior frameworks to enable concurrent unmanned aerial and ground vehicle operation in large-scale, photorealistic, dynamic environments. It introduces a new waypoint-tracking UGV controller that mirrors existing UAV control interfaces and provides a shared navigation stack for mapping, traversability analysis, planning, and control across heterogeneous platforms. Expanding inherited sensor suites, it adds physics-based long-wave infrared cameras and configurable night-vision modes. HERCULES supplies lightweight APIs, ROS 2 wrappers, and rigorous time synchronization, integrates in

Load-bearing premise

The new waypoint-tracking UGV controller accurately mirrors existing UAV control interfaces and the shared navigation stack enables effective mapping, traversability analysis, planning, and control across heterogeneous platforms without major synchronization or fidelity issues.

Editorial extensions

If this is right

  • Supports generation of reproducible multi-modal datasets by replaying offline-designed trajectories across multiple robots.
  • Enables active online planning in closed loop from live sensor observations in heterogeneous teams.
  • Facilitates experiments in degraded visual environments through added LWIR cameras and night-vision modes.
  • Integrates dynamic elements including pedestrians, traffic, wildlife, fire, flooding, and crop disease spread into robot autonomy tests.
  • Supplies a heterogeneous multi-robot SLAM benchmark collected with two UAVs and two UGVs across kilometer-scale desert, forest, and city environments.

Reading between the lines

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

  • The released datasets could serve as a common reference for comparing new collaborative perception algorithms across research groups without requiring physical hardware.
  • Support for dynamic phenomena such as flooding and fire spread may allow simulation of disaster-response scenarios to evaluate exploration strategies.
  • The framework's architecture could be extended to additional robot types or sensor modalities to test broader multi-robot coordination questions.
  • Public availability of code and benchmarks may encourage standardized evaluation protocols for heterogeneous autonomy research.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 2 minor

Summary. The manuscript presents HERCULES, an open-source simulation framework extending AirSim and Cosys-AirSim on Unreal Engine 5 to support concurrent UAV-UGV operations in large-scale, photorealistic, dynamic environments. Key additions include a waypoint-tracking UGV controller mirroring UAV interfaces, a shared navigation stack for mapping/traversability/planning/control, expanded sensors (LWIR cameras, night-vision modes), ROS 2 wrappers, time synchronization, and integration of dynamic agents/phenomena (pedestrians, fire, flooding). The framework supports passive trajectory replay for dataset generation and active closed-loop planning; experiments demonstrate utility for heterogeneous multi-robot SLAM, collaborative perception, and exploration, with public release of code, documentation, and a kilometer-scale benchmark dataset across desert/forest/city settings.

Significance. If the framework performs as described, the work is a useful open-source contribution to robotics simulation research. The public release of source code, experiment code, documentation, and datasets (including the heterogeneous SLAM benchmark) enables direct verification and community extension, which strengthens the paper. Integration of physics-based sensors and dynamic environmental phenomena addresses gaps in prior heterogeneous-robot simulators.

minor comments (2)
  1. [Abstract] Abstract: the statement that HERCULES 'resolves key architectural limitations of prior frameworks' would be strengthened by a brief explicit comparison (e.g., a short table) to the limitations of AirSim/Cosys-AirSim and other cited simulators.
  2. [Experiments] Experiments section: while utility is demonstrated, inclusion of quantitative baselines or ablation results (e.g., SLAM accuracy metrics with/without the shared navigation stack) would make the performance claims more concrete.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive evaluation of the manuscript, recognition of its contributions to heterogeneous multi-robot simulation, and recommendation to accept. We are pleased that the open-source release, benchmark datasets, and integration of dynamic phenomena were viewed as strengthening the work.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

The paper is a tool-release describing an open-source simulator built on AirSim/Cosys-AirSim. It introduces a waypoint-tracking UGV controller, shared navigation stack, LWIR sensors, and ROS 2 wrappers, with experiments in SLAM, perception, and exploration. No equations, fitted parameters, predictions, or derivation chains appear. Central claims rest on released code, time-synchronization details, and empirical utility demonstrations rather than self-referential reductions or load-bearing self-citations. No steps match any enumerated circularity pattern.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The paper is a software framework contribution with no free parameters, no invented physical entities, and only standard domain assumptions about game-engine fidelity.

assumptions (1)
  • domain assumption Unreal Engine 5 and AirSim provide a sufficiently accurate base for photorealistic rendering, physics, and sensor simulation in robotics contexts
    The entire framework is built upon these existing simulators as stated in the abstract.

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Cite this review

Pith. "Pith review of HERCULES: An Open-Source Simulation Framework for Heterogeneous Multi-Robot SLAM, Collaborative Perception, and Exploration." pith.science (2026). https://pith.science/paper/2U35FM4M

@misc{pith2026260622756,
  author       = {Pith},
  title        = {Pith review of: HERCULES: An Open-Source Simulation Framework for Heterogeneous Multi-Robot SLAM, Collaborative Perception, and Exploration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2U35FM4M}},
  note         = {Machine review of arXiv:2606.22756}
}
read the original abstract

We present HERCULES, an open-source simulator and data-collection pipeline for heterogeneous multi-robot autonomy. Built upon the Unreal Engine 5 (UE5)-based simulators AirSim and Cosys-AirSim, HERCULES resolves key architectural limitations of prior frameworks to enable concurrent unmanned aerial and ground vehicle (UAV-UGV) operation in large-scale, photorealistic, dynamic environments. It introduces a new waypoint-tracking UGV controller that mirrors existing UAV control interfaces, and provides a shared navigation stack for mapping, traversability analysis, planning, and control across heterogeneous platforms. Expanding inherited sensor suites, it adds physics-based long-wave infrared (LWIR) cameras and configurable night-vision modes for degraded visual environments. HERCULES provides lightweight APIs, ROS 2 wrappers, and rigorous time synchronization across sensors and platforms, and brings state-of-the-art game-engine capabilities into robotics simulation, integrating intelligent agents such as pedestrians, traffic, and wildlife with high-fidelity dynamic phenomena, including fire, flooding, and crop disease spread. HERCULES runs in two modes: passively, replaying offline-designed trajectories to generate reproducible multi-modal datasets, and actively, running an online planner in closed loop from live observations. Our experiments in heterogeneous multi-robot SLAM, collaborative perception, and exploration, using both HERCULES-generated data and active closed-loop execution, demonstrate its utility for advancing heterogeneous multi-robot autonomy. We publicly release our source code, experiment code, documentation, and datasets, including a heterogeneous multi-robot SLAM benchmark collected with two UAVs and two UGVs across kilometer-scale desert, forest, and city environments, at https://lunarlab-gatech.github.io/HERCULES-website.

Figures

Figures reproduced from arXiv: 2606.22756 by the authors.

Figure 1
Figure 1. Environment diversity in HERCULES at two operational scales. (Left column) Ground-level detail. (Right column) High-level overview. (Top row) Desert. (Middle row) Forest. (Bottom row) City. Dynamic agents (AnimalAI wildlife, MetaHuman pedestrians, VehicleAI traffic) can be toggled based on experimental requirements. (UE5) (Epic Games 2026b) by extending AirSim (Shah et al. 2018) and Cosys-AirSim (Jansen et al. 2023)… view at source ↗
Figure 2
Figure 2. An overview of HERCULES, a UE5-based simulator and experimentation stack for heterogeneous UAV–UGV autonomy. HERCULES provides photorealistic large-scale worlds and synchronized sensing with ready-to-run interfaces, benchmarks, and dataset export for collaborative SLAM, cooperative perception, and exploration. The Heterogeneous Multi-Robot Workflows panel marks the capabilities quantitatively evaluated in this paper… view at source ↗
Figure 4
Figure 4. Night-vision goggle (NVG) rendering in the desert environment. (Top left) RGB image under near-zero ambient illumination where the scene is essentially dark. (Top right) Corresponding NVG image revealing terrain and vegetation structure. (Bottom left) RGB image with an active fire source. (Bottom right) NVG response to the same configuration, showing realistic saturation, blooming, and brightness clipping around the… view at source ↗
Figures from the paper (10 more)
Figure 5
Figure 5. Figure 5: Dynamic environmental phenomena implemented in HERCULES. (Top) Wildfire spread with progressive smoke propagation. (Middle) Flood inundation on a geo-registered model of the Georgia Tech campus. (Bottom) Crop disease transmission across agricultural terrain. the enviro…
Figure 6
Figure 6. Figure 6: Ground-truth map generation pipeline. Detailed UE5 environments (left) are converted into ground-truth OctoMaps (center), followed by elevation maps at user-specified altitudes (right). This unified mapping pipeline can be run offline for full-environment preprocessing…
Figure 7
Figure 7. Figure 7: Operational modes for heterogeneous UAV–UGV teams in HERCULES. (a) Complementary Coverage disperses agents to improve coverage and reduce uncertainty. (b) Leader-Follower enforces spatiotemporal overlap for cooperative perception. Legend applies to both panels. spread …
Figure 8
Figure 8. Figure 8: Representative views from each sequence in the collaborative SLAM experiment, with (Top) UAV aerial views and (Bottom) UGV ground views. Each sequence poses different challenges for multi-agent localization as we rely on HERCULES’s capabilities for photorealistic light…
Figure 9
Figure 9. Figure 9: Object maps constructed by ROMAN and the corresponding robot trajectory overlaid onto the environment point cloud. The environments are as follows: (Left) City, (Middle) Desert, and (Right) Forest. Object point colors are included for visual clarity. We provide views o…
Figure 10
Figure 10. Figure 10: Ground truth (GT) versus estimated (Est.) trajectories of various sequences in [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Representative sample from the HERCULES cooperative vehicle–infrastructure dataset. (Left) Third-person view showing the heterogeneous robot team: a UAV provides overhead infrastructure-view sensing while a UGV navigates at street level among dynamic traffic and pedes…
Figure 12
Figure 12. Figure 12: Qualitative cooperative 3D Car detection late-fusion results on the DAIR-V2X test set. (Top left) Ground-truth annotations in 3D and BEV. (Top right) Late-fusion detections combining vehicle and infrastructure views. (Bottom left) Vehicle-only detections. (Bottom righ…
Figure 13
Figure 13. Figure 13: Representative single-run UAV (red) and UGV (green) trajectories in the desert environment under the two coordination modes. The divergence in (a) versus the overlap in (b) is the qualitative mechanism behind the inter-agent overlap η reported in [PITH_FULL_IMAGE:fig…
Figure 14
Figure 14. Figure 14: Ground-truth coverage C(t) versus mission time for the heterogeneous UAV–UGV team in the desert environment, over n = 8 seeds per mode (median, with shaded interquartile range). Complementary Coverage attains consistently higher coverage at equal mission time than Lea…

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Works this paper leans on

47 extracted references · 2 canonical work pages

  1. [1]

    ArduPilot Dev Team (2026) Using SITL with Gazebo . Online. ://ardupilot.org/dev/docs/sitl-with-gazebo.html. Accessed: 2026-01-27

    2026

  2. [2]

    IEEE Transactions on Robotics 37(6): 1874--1890

    Campos C, Elvira R, Rodríguez JJG, M Montiel JM and D Tardós J (2021) ORB-SLAM3 : An accurate open-source library for visual, visual–inertial, and multimap SLAM . IEEE Transactions on Robotics 37(6): 1874--1890

    2021

  3. [3]

    Cesium GS, Inc (2026) Cesium for Unreal . Online. ://cesium.com/platform/cesium-for-unreal/. Accessed: 2026-01-22

    2026

  4. [4]

    IEEE Robotics and Automation Letters

    Cui C, Zhou X, Wang M, Gao F and Xu C (2024) FastSim : A modular and plug-and-play simulator for aerial robots. IEEE Robotics and Automation Letters

    2024

  5. [5]

    In: Proceedings of the Conference on Robot Learning

    Dosovitskiy A, Ros G, Codevilla F, Lopez A and Koltun V (2017) CARLA : An open urban driving simulator. In: Proceedings of the Conference on Robot Learning. pp. 1--16

    2017

  6. [6]

    In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

    Dubois R, Eudes A and Frémont V (2020) AirMuseum : a heterogeneous multi-robot dataset for stereo-visual and inertial simultaneous localization and mapping. In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems . pp. 166--172

    2020

  7. [7]

    Epic Games (2021) Nanite virtualized geometry in Unreal Engine . Online. ://dev.epicgames.com/documentation/en-us/unreal-engine/nanite-virtualized-geometry-in-unreal-engine. Accessed: 2026-01-27

    2021

  8. [8]

    Epic Games (2022) Lumen global illumination and reflections in Unreal Engine . Online. ://dev.epicgames.com/documentation/en-us/unreal-engine/lumen-global-illumination-and-reflections-in-unreal-engine. Accessed: 2026-01-27

    2022

Show all 47 references
  1. [9]

    Epic Games (2024) Artificial intelligence in Unreal Engine 5: Behavior trees, blackboards and AI controllers. Online. ://dev.epicgames.com/documentation/en-us/unreal-engine/behavior-tree-in-unreal-engine---user-guide. Accessed: 2026-01-27

    2024

  2. [10]

    https://www.fab.com/

    Epic Games (2026 a ) Fab. https://www.fab.com/. Accessed: 2026-06-15

    2026

  3. [11]

    Epic Games (2026 b ) Unreal Engine 5 documentation. Online. ://www.unrealengine.com. Accessed: 2026-01-22

    2026

  4. [12]

    In: Robot Operating System (ROS): The Complete Reference (Volume 1)

    Furrer F, Burri M, Achtelik M and Siegwart R (2016) RotorS : A modular Gazebo MAV simulator framework. In: Robot Operating System (ROS): The Complete Reference (Volume 1). Springer International Publishing, pp. 595--625

    2016

  5. [13]

    In: Proceedings of the IEEE International Conference on Robotics and Automation

    Geneva P, Eckenhoff K, Lee W, Yang Y and Huang G (2020) OpenVINS : A research platform for visual-inertial estimation. In: Proceedings of the IEEE International Conference on Robotics and Automation . pp. 4666--4672

    2020

  6. [14]

    Grupp M (2017) evo: Python package for the evaluation of odometry and SLAM . Online. ://github.com/MichaelGrupp/evo

    2017

  7. [15]

    In: Proceedings of the IEEE / RSJ International Conference on Intelligent Robots and Systems

    Guerra W, Tal E, Murali V, Ryou G and Karaman S (2019) FlightGoggles : Photorealistic Sensor Simulation for Perception-driven Robotics using Photogrammetry and Virtual Reality . In: Proceedings of the IEEE / RSJ International Conference on Intelligent Robots and Systems . pp. ...

    2019

  8. [16]

    International Journal of Robotics Research 41(3): 259--269

    Hall D, Talbot B, Bista SR, Zhang H, Smith R, Dayoub F and S \"u nderhauf N (2022) BenchBot environments for active robotics ( BEAR ): Simulated data for active scene understanding research. International Journal of Robotics Research 41(3): 259--269

    2022

  9. [17]

    Autonomous Robots 34(3): 189--206

    Hornung A, Wurm KM, Bennewitz M, Stachniss C and Burgard W (2013) OctoMap : An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots 34(3): 189--206

    2013

  10. [18]

    In: Proceedings of the Annual Modeling and Simulation Conference

    Jansen W, Verreycken E, Schenck A, Blanquart JE, Verhulst C, Huebel N and Steckel J (2023) Cosys-AirSim : A real-time simulation framework expanded for complex industrial applications. In: Proceedings of the Annual Modeling and Simulation Conference. pp. 37--48

    2023

  11. [19]

    In: Proceedings of the Annual RoboCup International Symposium

    Kohlbrecher S, Meyer J, Graber T, Petersen K, Klingauf U and Von Stryk O (2013) Hector open source modules for autonomous mapping and navigation with rescue robots. In: Proceedings of the Annual RoboCup International Symposium. pp. 624--631

    2013

  12. [20]

    SPIE, pp

    Kooi FL and Toet A (2005) What's crucial in night vision goggle simulation? In: Enhanced and Synthetic Vision 2005, volume 5802. SPIE, pp. 37--46. doi:10.1117/12.601432

    2005 doi

  13. [21]

    In: Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition

    Lang AH, Vora S, Caesar H, Zhou L, Yang J and Beijbom O (2019) PointPillars : Fast encoders for object detection from point clouds. In: Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition . pp. 12697--12705

    2019

  14. [22]

    International Journal of Robotics Research 20(5): 378--400

    LaValle SM and Kuffner Jr JJ (2001) Randomized kinodynamic planning. International Journal of Robotics Research 20(5): 378--400

    2001

  15. [23]

    In: Proceedings of the Conference on Robot Learning, volume 164

    Li C, Xia F, Mart \' n-Mart \' n R, Lingelbach M, Srivastava S, Shen B, Vainio KE, Gokmen C, Dharan G, Jain T, Kurenkov A, Liu K, Gweon H, Wu J, Fei-Fei L and Savarese S (2022) iGibson 2.0 : Object-centric simulation for robot learning of everyday household tasks. In: Proceedi...

    2022

  16. [24]

    SmartBot 1(3)

    Li D, Shi H, Cai B, Bai X, Chen L, Han G and Mi C (2025) A review of technical advances and applications of intelligent inspection robots in structural health monitoring. SmartBot 1(3)

    2025

  17. [25]

    In: Proceedings of the IEEE International Conference on Robotics and Automation

    Meier L, Honegger D and Pollefeys M (2015) PX4 : A node-based multithreaded open source robotics framework for deeply embedded platforms. In: Proceedings of the IEEE International Conference on Robotics and Automation . pp. 6235--6240

    2015

  18. [26]

    webots™: professional mobile robot simulation

    Michel O (2004) Cyberbotics ltd. webots™: professional mobile robot simulation. International Journal of Advanced Robotic Systems 1(1): 5

    2004

  19. [27]

    Min P (2026) binvox: 3d mesh voxelizer. Online. ://www.patrickmin.com/binvox/. Accessed: 2026-01-22

    2026

  20. [28]

    Academic press

    Modest MF and Mazumder S (2021) Radiative heat transfer. Academic press

    2021

  21. [29]

    Journal of Sensor and Actuator Networks 13(6): 81

    Munasinghe I, Perera A and Deo RC (2024) A comprehensive review of UAV-UGV collaboration: Advancements and challenges. Journal of Sensor and Actuator Networks 13(6): 81

    2024

  22. [30]

    ://developer.nvidia.com/isaac-sim

    NVIDIA (2022) NVIDIA Isaac Sim . ://developer.nvidia.com/isaac-sim. Accessed: 2026-01-27

    2022

  23. [31]

    Open Robotics (2026) Gazebo . Online. ://gazebosim.org/. Accessed: 2026-01-22

    2026

  24. [32]

    In: Proceedings of the Robotics: Science and Systems Conference

    Peterson MB, Jia YX, Tian Y, Thomas A and How JP (2025) ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization . In: Proceedings of the Robotics: Science and Systems Conference

    2025

  25. [33]

    (2012) Argos: a modular, parallel, multi-engine simulator for multi-robot systems

    Pinciroli C, Trianni V, O’Grady R, Pini G, Brutschy A, Brambilla M, Mathews N, Ferrante E, Di Caro G, Ducatelle F et al. (2012) Argos: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm intelligence 6: 271--295

    2012

  26. [34]

    arXiv Preprint Poster at the Twelfth International Conference on Learning Representations

    Puig X, Undersander E, Szot A, Cote MD, Yang TY, Partsey R, Desai R, Clegg AW, Hlavac M, Min SY, Vondru s V, Gervet T, Berges VP, Turner JM, Maksymets O, Kira Z, Kalakrishnan M, Malik J, Chaplot DS, Jain U, Batra D, Rai A and Mottaghi R (2023) Habitat 3.0: A co-habitat for hum...

    2023

  27. [35]

    IEEE Access 8: 191617--191643

    Queralta JP, Taipalmaa J, Pullinen BC, Sarker VK, Gia TN, Tenhunen H, Gabbouj M, Raitoharju J and Westerlund T (2020) Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. IEEE Access 8: 191617--191643

    2020

  28. [36]

    In: Proceedings of the International Conference on Field and Service Robotics

    Shah S, Dey D, Lovett C and Kapoor A (2018) AirSim : High-fidelity visual and physical simulation for autonomous vehicles. In: Proceedings of the International Conference on Field and Service Robotics. pp. 621--635

    2018

  29. [37]

    In: Proceedings of the IEEE / RSJ International Conference on Intelligent Robots and Systems

    Shan T, Englot B, Meyers D, Wang W, Ratti C and Rus D (2020) LIO-SAM : Tightly-coupled lidar inertial odometry via smoothing and mapping. In: Proceedings of the IEEE / RSJ International Conference on Intelligent Robots and Systems . pp. 5135--5142

    2020

  30. [38]

    In: Proceedings of the Conference on Robot Learning

    Song Y, Naji S, Kaufmann E, Loquercio A and Scaramuzza D (2021) Flightmare: A flexible quadrotor simulator. In: Proceedings of the Conference on Robot Learning. pp. 1147--1157

    2021

  31. [39]

    IEEE Transactions on Robotics 38(4)

    Tian Y, Chang Y, Arias FH, Nieto-Granda C, How JP and Carlone L (2022) Kimera- M ulti: Robust, distributed, dense metric-semantic SLAM for multi-robot systems. IEEE Transactions on Robotics 38(4)

    2022

  32. [40]

    Academic Press

    Tiwari K and Chong NY (2019) Multi-robot exploration for environmental monitoring: the resource constrained perspective. Academic Press

    2019

  33. [41]

    IEEE Transactions on Pattern Analysis and Machine Intelligence 13(04): 376--380

    Umeyama S (1991) Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(04): 376--380

    1991

  34. [42]

    IEEE Transactions on Robotics

    Xu Z, Garimella SS and Tzoumas V (2025) Communication- and computation-efficient distributed submodular optimization in robot mesh networks. IEEE Transactions on Robotics

    2025

  35. [43]

    In: Proceedings of the International Conference on Computer Simulation and Modeling, Information Security

    Xue H and Shi Z (2023) Research on simulation experiment of UAV crossing fire circle based on AirSim . In: Proceedings of the International Conference on Computer Simulation and Modeling, Information Security. pp. 203--209

    2023

  36. [44]

    In: Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition

    Yu H, Luo Y, Shu M, Huo Y, Yang Z, Shi Y, Guo Z, Li H, Hu X, Yuan J and Nie Z (2022) DAIR-V2X : A large-scale dataset for vehicle-infrastructure cooperative 3D object detection. In: Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition . pp. 21361--21370

    2022

  37. [45]

    arXiv Preprint (2306.12156)

    Zhao X, Ding W, An Y, Du Y, Yu T, Li M, Tang M and Wang J (2023) Fast segment anything. arXiv Preprint (2306.12156)

    2023

  38. [46]

    IEEE Robotics and Automation Letters 9(7): 6416--6423

    Zhou Y, Quang L, Nieto-Granda C and Loianno G (2024) CoPeD -advancing multi-robot collaborative perception: A comprehensive dataset in real-world environments. IEEE Robotics and Automation Letters 9(7): 6416--6423

    2024

  39. [47]

    IEEE Robotics and Automation Letters 8(2): 966--973

    Zhu Y, Kong Y, Jie Y, Xu S and Cheng H (2023) GRACO : A multimodal dataset for ground and aerial cooperative localization and mapping. IEEE Robotics and Automation Letters 8(2): 966--973

    2023

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