发表机构
AI Chip Center for Emerging Smart Systems; InnoHK Centers; HongKong University of Science and Technology; Southeast University; City University of Hong Kong; Huazhong University of Science and Technology; University of Hong Kong; Wuhan University（新兴智能系统AI芯片中心; 创新香港中心; 香港科技大学; 东南大学; 香港城市大学; 华中科技大学; 香港大学; 武汉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出RAEM框架，通过混合局部-全局可通行性表示、楼梯中心对齐策略与双路径搜索机制，实现四足机器人在多层级环境的鲁棒自主探索，经仿真与真实实验验证有效。
AI 中文摘要
本文提出了RAEM，一种面向在多层级环境中作业的四足机器人的鲁棒自主探索框架。现有的大多数地面机器人探索方法依赖于平面可通行性表示，无法充分表征多层建筑的重叠结构与跨楼层连通性。尽管基于层析成像的表示可为多层导航提供有效的可通行性建模，但维护全局层析成像图会为频繁重规划的在线探索带来巨大计算开销。此外，楼梯间稀疏且碎片化的激光雷达观测会降低局部可通行性估计精度，导致视点放置不规则及临时拓扑断开。为应对这些挑战，RAEM采用混合局部-全局可通行性表示：使用局部层析成像图与显式分类的局部3D栅格图进行在线地形分析与连通性评估，同时从这些局部空间表示中增量构建高程感知的全局拓扑图，以实现高效的跨楼层探索规划。我们进一步引入楼梯中心对齐策略以减少攀爬过程中的偏航突变，以及双路径搜索机制，用于在全局拓扑局部断开时恢复引导路径。大量仿真与真实世界实验表明，RAEM可在多层级结构中实现鲁棒且计算稳定的自主探索，包括对五层楼梯间的连续探索。
英文摘要
In this paper, we propose RAEM, a robust autonomous exploration framework for quadruped robots operating in multi-floor environments. Most existing ground-robot exploration approaches rely on planar traversability representations, which cannot adequately represent the overlapping structures and cross-floor connectivity of multi-floor buildings. Although tomography-based representations provide effective traversability modeling for multi-floor navigation, maintaining a global tomography map incurs substantial computational overhead for online exploration with frequent replanning. Moreover, sparse and fragmented LiDAR observations in stairwells can degrade local traversability estimation, leading to irregular viewpoint placement and temporary topological disconnections. To address these challenges, RAEM adopts a hybrid local-global traversability representation, in which a local tomography map and an explicitly categorized local 3D grid map are used for online terrain analysis and connectivity evaluation, while an elevation-aware global topological graph is incrementally constructed from these local spatial representations for efficient cross-floor exploration planning. We further introduce a staircase center alignment strategy to reduce abrupt yaw variations during climbing and a dual path searching mechanism to recover guidance paths when the global topology is locally disconnected. Extensive simulation and real-world experiments demonstrate robust and computationally stable autonomous exploration across multi-floor structures, including continuous exploration of a five-floor stairwell.
Comments21 pages, 23 figures
