发表机构
University of Luxembourg; IRL Georgia Tech-CNRS（卢森堡大学; 乔治亚理工学院-法国国家科学研究中心国际研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对自主漫游车稠密栅格代价图规划计算开销高的问题，提出STAG稀疏可通行性感知图，通过实验验证其可大幅降低规划时间与内存占用，实现高效机器人导航。
AI 中文摘要
在大型非结构化环境中导航的自主漫游车需要考虑地形可通行性的高效全局规划，但随着映射区域扩大，对基于栅格的稠密代价图进行搜索会产生高昂的计算开销。本文提出STAG，即稀疏可通行性感知图，它能将代价图转换为紧凑图表示。STAG结合了中轴拓扑主干、代表同质可通行性区域的节点以及靠近强可通行性梯度的过渡节点，边则编码几何信息与可通行性，以兼顾路径长度与地形难度。我们使用合成洞穴地图、矿山地图及DARPA CERBERUS数据集，在203个地图实例、101200次查询构成的5个基准类别中，对比STAG与稠密栅格上的A*算法：STAG将规划中位数时间降低3.4倍至9.9倍，峰值查询内存降低2.1倍至15.4倍，路径长度中位数相对差异为-2.9%至+7.6%。STAG为全局规划提供了紧凑表示，以牺牲稠密栅格的可通行性最优性为代价，实现了更快、内存占用更低的搜索。
英文摘要
Autonomous rovers navigating large unstructured environments need efficient global planning that accounts for terrain traversability. However, searching dense grid-based costmaps becomes computationally expensive as the mapped area grows. We introduce STAG, a Sparse Traversability-Aware Graph that converts costmaps into compact graphs. STAG combines a medial-axis topological backbone, representative nodes for homogeneous traversability regions, and transition nodes near strong traversability gradients. Edges encode geometry and traversability to account for path length and terrain difficulty. We compare A* on STAG and dense grids using synthetic cave maps, mine maps and the DARPA CERBERUS dataset. Across five benchmark categories comprising 203 map instances and 101,200 queries, STAG reduces median planning time by 3.4x to 9.9x and peak query memory by 2.1x to 15.4x, with median relative path-length differences of -2.9% and +7.6%. STAG offers a compact representation for global planning, trading dense-grid traversability optimality for faster, less memory-intensive search.
