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

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Yun Sil Chang , Kamak Ebadi , Christopher E. Denniston , Muhammad Fadhil Ginting , Antoni Rosinol , Andrzej Reinke , Matteo Palieri , Jingnan Shi , A. Chatterjee , Benjamin Morrell , Ali‐akbar Agha‐mohammadi , Luca Carlone ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2205.13135

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.

Related Topics

Odometry

Robot

Artificial Intelligence

Computer Science

Simultaneous Localization And Mapping

Concepts

Odometry Robot Artificial intelligence Computer science Simultaneous localization and mapping Computer vision Ground truth Scalability Context (archaeology) Search and rescue Urban search and rescue Lidar Terrain Real-time computing Mobile robot Remote sensing Geography Database Cartography Archaeology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2205.13135
PDF: https://arxiv.org/pdf/2205.13135
OA Status: green
Cited By: 2
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2205.13135

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

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-05-26

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Authors: Yun Sil Chang, Kamak Ebadi, Christopher E. Denniston, Muhammad Fadhil Ginting, Antoni Rosinol, Andrzej Reinke, Matteo Palieri, Jingnan Shi, A. Chatterjee, Benjamin Morrell, Ali‐akbar Agha‐mohammadi, Luca Carlone

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Concepts: Odometry, Robot, Artificial intelligence, Computer science, Simultaneous localization and mapping, Computer vision, Ground truth, Scalability, Context (archaeology), Search and rescue, Urban search and rescue, Lidar, Terrain, Real-time computing, Mobile robot, Remote sensing, Geography, Database, Cartography, Archaeology

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Cited by: 2

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Citations by year (recent): 2025: 1, 2024: 1

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