LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments

by   Yun Chang, et al.

Search and rescue with a team of heterogeneous mobile robots in unknown and large-scale underground environments requires high-precision localization and mapping. This crucial requirement is faced with many challenges in complex and perceptually-degraded subterranean environments, as the onboard perception system is required to operate in off-nominal conditions (poor visibility due to darkness and dust, rugged and muddy terrain, and the presence of self-similar and ambiguous scenes). In a disaster response scenario and in the absence of prior information about the environment, robots must rely on noisy sensor data and perform Simultaneous Localization and Mapping (SLAM) to build a 3D map of the environment and localize themselves and potential survivors. To that end, this paper reports on a multi-robot SLAM system developed by team CoSTAR in the context of the DARPA Subterranean Challenge. We extend our previous work, LAMP, by incorporating a single-robot front-end interface that is adaptable to different odometry sources and lidar configurations, a scalable multi-robot front-end to support inter- and intra-robot loop closure detection for large scale environments and multi-robot teams, and a robust back-end equipped with an outlier-resilient pose graph optimization based on Graduated Non-Convexity. We provide a detailed ablation study on the multi-robot front-end and back-end, and assess the overall system performance in challenging real-world datasets collected across mines, power plants, and caves in the United States. We also release our multi-robot back-end datasets (and the corresponding ground truth), which can serve as challenging benchmarks for large-scale underground SLAM.


page 1

page 7


LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, an...

DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

Enabling fully autonomous robots capable of navigating and exploring lar...

Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned

This paper revisits Kimera-Multi, a distributed multi-robot Simultaneous...

Blind as a bat: audible echolocation on small robots

For safe and efficient operation, mobile robots need to perceive their e...

Present and Future of SLAM in Extreme Underground Environments

This paper reports on the state of the art in underground SLAM by discus...

Smoothing and Mapping using Multiple Robots

Mapping expansive regions is an arduous and often times incomplete when ...

DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm

To execute collaborative tasks in unknown environments, a robotic swarm ...

Please sign up or login with your details

Forgot password? Click here to reset