受限未知环境中基于全方位感知的姿态感知腿式机器人语义探索
Pose-aware Legged Robot Semantic Exploration with Omnidirectional Perception in Confined Unknown Environments
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中文总结 AI 辅助
针对受限环境中腿式机器人语义探索的覆盖-效率权衡问题，提出姿态感知探索系统POSE，利用身体俯仰翻滚和全方位感知优化视点选择，显著提升覆盖率并减少探索时间。
中文摘要 AI 辅助
在受限环境中的语义探索需要同时进行环境建图和对目标物体的详细观察。对于地面机器人而言，有限的传感器垂直视场角和受限的驻留距离可能导致平面视角无法观察到物体的上表面。身体倾斜可以改善覆盖范围，但额外的观察和姿态转换会增加任务时间。为了解决这一权衡问题，我们提出了POSE，一种姿态感知的语义探索系统，利用腿式机器人固有的身体俯仰和翻滚以及全方位相机-激光雷达感知。所提出的姿态感知视点采样模块根据预期覆盖增益从部分物体地图中选择身体姿态，而目标对齐执行减少了不必要的身体重新定向。此外，我们引入了一种由视觉语言模型（VLM）辅助的以物体为中心的视点剪枝策略，该策略利用持久观察历史和鸟瞰图（BEV）地图来减少冗余的检查访问。由此产生的语义视点与几何探索视点在全局探索规划器中相结合。仿真表明，与平面规划基线相比，POSE将最终目标表面覆盖率提高了8-10个百分点，同时将探索时间减少了17-32%，并在评估的基线中实现了最高的平均物体覆盖AUC。在机器车间中携带全方位相机-激光雷达套件的腿式机器人进行的真实世界实验进一步证明了该系统的适用性。这些结果支持自适应身体姿态规划，以改善腿式机器人语义探索中的覆盖-效率权衡。我们计划在未来发布代码以造福社区。
英文摘要
Semantic exploration in confined environments requires both environment mapping and detailed observation of target objects. For ground robots, limited sensor vertical fields of view and restricted standoff distances can leave upper object surfaces unobserved from planar viewpoints. Body tilting can improve coverage, but additional observations and posture transitions increase mission time. To address this trade-off, we present POSE, a pose-aware semantic exploration system that exploits a legged robot's intrinsic body pitch and roll with omnidirectional camera-LiDAR perception. The proposed pose-aware viewpoint sampling module selects body postures from partial object maps according to expected coverage gain, while aim-aligned execution reduces unnecessary body reorientation. Further, we introduce an object-centric viewpoint pruning strategy assisted by a vision-language model (VLM), which uses persistent observation history and bird's-eye-view (BEV) maps to reduce redundant inspection visits. The resulting semantic viewpoints are combined with geometric exploration viewpoints in a global exploration planner. Simulations show that POSE improves final target-surface coverage by 8-10 percentage points over the planar planning baseline while reducing exploration time by 17-32%, and achieves the highest mean object coverage AUC among the evaluated baselines. Real-world experiments with a legged robot carrying an omnidirectional camera-LiDAR suite in a machine shop further demonstrate the system's applicability. These results support adaptive body-posture planning for improving the coverage-efficiency trade-off in legged robot semantic exploration. We plan to release the code for community benefit in the future.
发表机构
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
