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

arXiv: https://arxiv.org/abs/2201.07067 · HTML: https://mlsanity.com/paper/2201.07067/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2022.

## Built on by (most cited first, 17 of 17)

- [Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding](https://mlsanity.com/paper/2402.03947/) (2024)
- [Learning-based Localizability Estimation for Robust LiDAR Localization](https://mlsanity.com/paper/2203.05698/) (2022)
- [UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT](https://mlsanity.com/paper/2206.08185/) (2022)
- [Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation](https://mlsanity.com/paper/2204.08647/) (2022)
- [Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models](https://mlsanity.com/paper/2309.10900/) (2023)
- [Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments](https://mlsanity.com/paper/2304.08908/) (2023)
- [Online Refractive Camera Model Calibration in Visual Inertial Odometry](https://mlsanity.com/paper/2409.12074/) (2024)
- [Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots](https://mlsanity.com/paper/2506.05516/) (2025)
- [Copiloting Autonomous Multi-Robot Missions: A Game-inspired Supervisory Control Interface](https://mlsanity.com/paper/2204.06647/) (2022)
- [Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments](https://mlsanity.com/paper/2206.10062/) (2022)
- [Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities](https://mlsanity.com/paper/2210.09678/) (2022)
- [Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration](https://mlsanity.com/paper/2304.14520/) (2023)
- [Map Prediction and Generative Entropy for Multi-Agent Exploration](https://mlsanity.com/paper/2501.13189/) (2025)
- [Scaling 3D Reasoning with LMMs to Large Robot Mission Environments Using Datagraphs](https://mlsanity.com/paper/2407.10743/) (2024)
- [CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments](https://mlsanity.com/paper/2505.06483/) (2025)
- [Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements](https://mlsanity.com/paper/2603.22182/) (2026)
- [Beyond Frontiers: Scene-Anomaly Guided Autonomous Exploration](https://mlsanity.com/paper/2607.15828/) (2026)

## 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.