CompSLAM通过多模态传感器融合实现地下环境高鲁棒定位与建图。
CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments
- 融合多种传感器的互补信息，构建分层冗余定位框架。
- 在740米复杂地下环境中实现稳定定位，光照与结构变化下表现优异。
- 适合无人机器人在无GPS环境下执行探索任务，支持多机协同建图。
在未知、无GPS且环境复杂的地下场景中，机器人自主需实时、鲁棒、精确的本地化与建图能力。在黑暗、粉尘及几何自相似结构等恶劣条件下，感知性能显著下降，使该任务尤为困难。本文提出CompSLAM，一种高度鲁棒的分层多模态定位与建图框架，通过利用不同传感器模态的互补性实现冗余容错。该系统在DARPA Subterranean Challenge期间成功部署于团队Cerberus的所有空中、腿式和轮式机器人，并在决赛中实现冠军表现。后续项目中亦验证其作为可靠里程计与建图方案的有效性，扩展支持多机器人地图共享与协作建图。本文还发布了一个由人工遥控四足机器人采集的全面数据集，覆盖了DARPA SubT Finals赛道的大部分区域，用于评估在740米长、几何结构多变、光照条件严苛环境下的鲁棒性。相关代码与数据集已公开，供机器人社区使用。
原文摘要 · Abstract (English)
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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