面向自然户外环境实时同步规划与建图的分层分辨率体积建图
Tiered-Resolution Volumetric Mapping for Real-Time Simultaneous Planning and Mapping on Natural Outdoor Environments
AI总结：
该研究针对户外规划机器人的实时建图成本问题，提出分层分辨率体积建图机制，通过规划与建图的反馈循环，在保证路径质量的同时大幅减小地图尺寸、降低CPU时间开销。
摘要：
规划机器人的实时体积建图主要受限于以最高分辨率整合与存储信息的成本，然而任何地图中仅有一小部分位于规划器实际考虑的路径上或其附近。我们利用这一点提出了一种分层分辨率建图机制，该机制将LiDAR数据同时整合到单一地图结构的两种分辨率中：在机器人当前规划路径周围的走廊内采用精细分辨率，其余区域采用粗略分辨率。与传统导航流水线中路径规划使用建图结果且不提供反馈的方式不同，我们提出了一种反馈循环，其中规划也会为建图提供信息，以动态整合不同叶分辨率的信息。在两个真实世界的远程操作数据集上，通过与独立的全分辨率地图进行离线重放和评分，分层地图比均匀精细地图小13-15倍，所需的整合CPU时间少6-8倍，同时在两种场景下生成的路径每米成本与从精细分辨率地图生成的路径相似。均匀粗略地图虽能达到相同的尺寸节省，但在更具挑战性的场景中其路径质量崩溃（每米成本增加210%），因为它对机器人必须穿越的地形过度离散化。分层机制近似了粗略地图的低成本，同时避免了其失效模式。
英文摘要：
Real-time volumetric mapping for a planning robot is dominated by the cost of integrating and storing information at the finest resolution, yet only a small fraction of any map ever lies on or near a path the planner actually considers. We exploit this with a tiered-resolution mapping mechanism that integrates LiDAR into a single map structure at two resolutions simultaneously: fine inside a corridor around the robot's current plan, and coarse everywhere else. As opposed to traditional navigation pipelines where path planning uses the mapping result and provides no feedback, we propose a feedback loop where planning also informs mapping to dynamically integrate information at different leaf resolutions. Across two real-world teleoperated datasets, replayed and scored offline against an independent full-resolution map, the tiered maps are 13-15x smaller and require 6-8x less integration CPU time than a uniform fine map, while producing paths at a similar cost per meter as the ones generated from fine resolution maps on both scenarios. A uniform coarse map reaches the same size savings but its path quality collapses (+210% cost per meter) on the harder scenario, because it over-discretizes terrain the robot must traverse. Tiering approximates the coarse map's cheapness without its failure mode.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
