mlsanityData to 7 Oct 2026

CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge

arXiv 2201.07067 ↗First posted 18 Jan 2022cs.ROcited by 15

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Abstract (arXiv)

Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems as underground settings present key challenges that can render robot autonomy hard to achieve. This has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, the CERBERUS system-of-systems is presented as a unified strategy towards subterranean exploration using legged and flying robots. As primary robots, ANYmal quadruped systems are deployed considering their endurance and potential to traverse challenging terrain. For aerial robots, both conventional and collision-tolerant multirotors are utilized to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, a complementary multi-modal sensor fusion approach utilizing camera, LiDAR, and inertial data for resilient robot pose estimation is proposed. Individual robot pose estimates are refined by a centralized multi-robot map optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined coordinate frame. Furthermore, a unified exploration path planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication between the robots and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station, alongside breadcrumbing of wireless nodes by our legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge Tunnel and Urban Circuits, along with the current limitations and the lessons learned for the benefit of the community.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jan 2022.

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Citing papers per year: 2022: 6; 2023: 3; 2024: 2; 2025: 3; 2026: 1.

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  1. 2022Learning-based Localizability Estimation for Robust LiDAR Localizationcited by 11
  2. 2022UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubTcited by 7
  3. 2022Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigationcited by 6
  4. 2023Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Modelscited by 5
  5. 2023Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environmentscited by 3
  6. 2024Online Refractive Camera Model Calibration in Visual Inertial Odometrycited by 2
  7. 2022Copiloting Autonomous Multi-Robot Missions: A Game-inspired Supervisory Control Interfacecited by 1
  8. 2022Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environmentscited by 1
  9. 2022Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilitiescited by 1
  10. 2023Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Explorationcited by 1
  11. 2025Map Prediction and Generative Entropy for Multi-Agent Explorationcited by 1
  12. 2025Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robotscited by 1
3 more
  1. 2024Scaling 3D Reasoning with LMMs to Large Robot Mission Environments Using Datagraphscited by 0
  2. 2025CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environmentscited by 0
  3. 2026Beyond Frontiers: Scene-Anomaly Guided Autonomous Explorationcited by 0