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

• Main Authors: Chang, Yun (Author), Ebadi, Kamak (Author), Denniston, Christopher E (Author), Ginting, Muhammad Fadhil (Author), Rosinol, Antoni (Author), Reinke, Andrzej (Author), Palieri, Matteo (Author), Shi, Jingnan (Author), Chatterjee, Arghya (Author), Morrell, Benjamin (Author), Agha-mohammad (Author), Carlone, Luca (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE), 2022-09-07T18:08:18Z.
• Subjects: Article
• Online Access: Get fulltext

 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.