# CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments

arXiv: https://arxiv.org/abs/2505.06483 · HTML: https://mlsanity.com/paper/2505.06483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 47 references that are in the map.
- First posted on arXiv 10 May 2025.

## Builds on (most cited first, 23 of 23)

- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments](https://mlsanity.com/paper/2104.14938/) (2021)
- [VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots](https://mlsanity.com/paper/2107.07243/) (2021)
- [Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction](https://mlsanity.com/paper/2203.03749/) (2022)
- [X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments](https://mlsanity.com/paper/2211.16335/) (2022)
- [Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots](https://mlsanity.com/paper/2307.10079/) (2023)
- [LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments](https://mlsanity.com/paper/2003.01744/) (2020)
- [CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge](https://mlsanity.com/paper/2201.07067/) (2022)
- [Self-supervised Learning of LiDAR Odometry for Robotic Applications](https://mlsanity.com/paper/2011.05418/) (2020)
- [Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned](https://mlsanity.com/paper/2207.04914/) (2022)
- [CELLO-3D: Estimating the Covariance of ICP in the Real World](https://mlsanity.com/paper/1810.01470/) (2018)
- [Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots](https://mlsanity.com/paper/2203.01389/) (2022)
- [Wildcat: Online Continuous-Time 3D Lidar-Inertial SLAM](https://mlsanity.com/paper/2205.12595/) (2022)
- [Learning-based Localizability Estimation for Robust LiDAR Localization](https://mlsanity.com/paper/2203.05698/) (2022)
- [DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments](https://mlsanity.com/paper/2102.05117/) (2021)
- [Keyframe-based Direct Thermal-Inertial Odometry](https://mlsanity.com/paper/1903.00798/) (2019)
- [Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments](https://mlsanity.com/paper/1903.01656/) (2019)
- [Informed, Constrained, Aligned: A Field Analysis on Degeneracy-aware Point Cloud Registration in the Wild](https://mlsanity.com/paper/2408.11809/) (2024)
- [Marsupial Walking-and-Flying Robotic Deployment for Collaborative Exploration of Unknown Environments](https://mlsanity.com/paper/2205.05477/) (2022)
- [A Framework for Collaborative Multi-Robot Mapping using Spectral Graph Wavelets](https://mlsanity.com/paper/2210.13856/) (2022)
- [Collaborative Robot Mapping using Spectral Graph Analysis](https://mlsanity.com/paper/2203.00308/) (2022)
- [Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs](https://mlsanity.com/paper/2504.06479/) (2025)

## Abstract (arXiv)

Robot autonomy in unknown, GPS-denied, and complex underground environments requires real-time, robust, and accurate onboard pose estimation and mapping for reliable operations. This becomes particularly challenging in perception-degraded subterranean conditions under harsh environmental factors, including darkness, dust, and geometrically self-similar structures. This paper details CompSLAM, a highly resilient and hierarchical multi-modal localization and mapping framework designed to address these challenges. Its flexible architecture achieves resilience through redundancy by leveraging the complementary nature of pose estimates derived from diverse sensor modalities. Developed during the DARPA Subterranean Challenge, CompSLAM was successfully deployed on all aerial, legged, and wheeled robots of Team Cerberus during their competition-winning final run. Furthermore, it has proven to be a reliable odometry and mapping solution in various subsequent projects, with extensions enabling multi-robot map sharing for marsupial robotic deployments and collaborative mapping. This paper also introduces a comprehensive dataset acquired by a manually teleoperated quadrupedal robot, covering a significant portion of the DARPA Subterranean Challenge finals course. This dataset evaluates CompSLAM's robustness to sensor degradations as the robot traverses 740 meters in an environment characterized by highly variable geometries and demanding lighting conditions. The CompSLAM code and the DARPA SubT Finals dataset are made publicly available for the benefit of the robotics community