重新思考自主主动建图中的学习占用率：基于观测门控滤波
Rethinking Learned Occupancy in Autonomous Active Mapping with Observation-Gated Filtering
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中文总结 AI 辅助
本研究通过闭环基准诊断学习占用率对主动建图规划的双重影响，提出观测门控滤波器以在观测不足时保留补全、重复暴露后抑制预测，无需重训即可改善易失败场景的覆盖率。
中文摘要 AI 辅助
自主三维主动建图要求空间机器人在构建导航所需几何结构的同时，选择感知位置。学习占用补全将空间上下文扩展到当前视野之外，但一个预测地图通常承担两个规划角色：它评估预期表面增益并约束无碰撞运动。因此，不支持的占用率可能同时扭曲机器人观察的位置及其认为可以通行的区域。我们在一个受控的闭环基准中研究这一耦合接口，保持主动建图系统固定，仅改变面向规划器的占用率条件，包括仅观测、学习、神谕修正和真实地面条件。提高占用率准确性并不会单调地改善闭环覆盖率：在25个起始点中，使用真实地面占用率进行规划，平均比学习基线提前12.7步达到最终覆盖率的70%，而最终覆盖率仅增加0.031。基于这一诊断，我们引入一种观测门控滤波器，在观测不足的区域保留补全，仅在重复视锥暴露且无邻近RGB-D支持后抑制预测。该滤波器无需重新训练或真实地面信息，即可改善针对性的易失败起始点。这些结果激励在通信窗口之间的自主间隔内，对面向规划器的几何结构进行在线修订。当前研究假设基准RGB-D观测和足够准确的姿态估计；行星感知条件和累积定位漂移仍有待评估。
英文摘要
Autonomous 3D active mapping requires a space robot to choose where to sense while building the geometry needed for navigation. Learned occupancy completion extends spatial context beyond the current field of view, but one predicted map often serves two planning roles: it scores expected surface gain and constrains collision-free motion. Unsupported occupancy can therefore distort both where the robot looks and where it believes it can travel. We study this coupled interface in a controlled closed-loop benchmark by holding the active-mapping system fixed and varying only its planner-facing occupancy across observation-only, learned, oracle-corrected, and ground-truth conditions. Improving occupancy accuracy does not monotonically improve closed-loop coverage: across 25 starts, planning with ground-truth occupancy reaches 70% of the learned baseline's final coverage 12.7 steps earlier on average, while increasing final coverage by only 0.031. Guided by this diagnosis, we introduce an observation-gated filter that retains completion in insufficiently observed regions and suppresses predictions only after repeated frustum exposure without nearby RGB-D support. The filter improves both targeted failure-prone starts without retraining or ground truth. These results motivate online revision of planner-facing geometry during autonomous intervals between communication windows. The current study assumes benchmark RGB-D observations and sufficiently accurate pose estimates; planetary sensing conditions and accumulated localization drift remain to be evaluated.
发表机构
- Boise State University（博伊西州立大学）
- Texas A&M University–San Antonio（德州农工大学圣安东尼奥分校）
机构由 AI 辅助整理，请以论文原文为准。
