STAG: A Sparse Traversability-Aware Graph Representation from Grid-Based Costmaps for Robotic Navigation

2026-10-08

AI Summary

This study addresses the excessive computational and memory overhead incurred by dense cost maps during path planning for autonomous rovers in unstructured environments. To this end, we propose the Sparse Traversability-Aware Graph (STAG) representation, which innovatively integrates a medial-axis topological skeleton, representative nodes for homogeneous regions, and strong-gradient transition nodes to transform dense grids into compact graph structures. By encoding geometric and traversability information within edges, STAG substantially compresses the search space. Extensive evaluations comprising over 100,000 queries across synthetic cave, mining, and DARPA datasets demonstrate that STAG reduces median planning time by 3.4× to 9.9× and peak memory consumption by 2.1× to 15.4×, while preserving near-optimal path quality.

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.

Problem

• Robotic Navigation
• Global Planning
• Traversability
• Costmaps
• Computational Efficiency

Innovation

• Sparse Graph Representation
• Traversability-Aware Planning
• Medial-Axis Topology
• Costmap Conversion
• Robotic Navigation