CompSLAM: 地下環境におけるロボット自律性のための相補的階層型マルチモーダル自己位置推定・地図構築
CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments
GPSが使えない地下環境で、複数のセンサ情報を相補的に統合して頑健な自己位置推定と地図構築を実現する階層型フレームワークCompSLAMを提案し、DARPA地下チャレンジ優勝チームの各種ロボットで実証した。
詳しい要約
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著者: Shehryar Khattak, Timon Homberger, Lukas Bernreiter, Julian Nubert, Olov Andersson, Roland Siegwart, Kostas Alexis, Marco Hutter
分類: cs.RO, cs.CV
原文アブストラクト
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
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