外观
论文arXiv 2610.1194320 天前
STAG：一种面向机器人导航、由基于网格的代价地图生成的稀疏可通行性感知图表示
原文链接：STAG: A Sparse Traversability-Aware Graph Representation from Grid-Based Costmaps for Robotic Navigation（arXiv 2610.11943 · 发布日期：2026-10-08）
STAG: A Sparse Traversability-Aware Graph Representation from Grid-Based Costmaps for Robotic Navigation
STAG将代价地图转为稀疏可通行图，在洞穴、矿井和DARPA CERBERUS地图上验证全局规划加速。
核心要点
- 大规模非结构环境中，稠密网格代价地图搜索会随建图范围增大而计算昂贵。
- STAG结合中轴拓扑骨架、均质可通行区域代表节点和梯度附近过渡节点。
- 边同时编码几何与可通行性，用于兼顾路径长度和地形通行难度。
- 在203个地图实例和101200次查询中，规划时间和峰值内存均显著降低。
验证范围与局限：验证范围为合成洞穴地图、矿井地图和DARPA CERBERUS数据集上的A*对比实验，体现以稠密网格可通行性最优性换取更快、更省内存搜索。
英文摘要 (Abstract)
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.
作者：Gabriel Manuel Garcia, Stéphanie Aravecchia, Miguel Angel Olivares-Mendez
来源
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