Beyond Geometry: Efficient Topologically-Grounded Navigation in Complex 3D Environments
Yifan Du, Chengwei Zhang, Siyu Liao, Zhongfeng Wang

TL;DR
This paper introduces a surface extraction framework for robot navigation in complex 3D environments, significantly reducing the search space and enabling fast, reliable path planning.
Contribution
It presents a novel method for constructing a reduced, topologically grounded state space that improves navigation efficiency in complex 3D scenes.
Findings
Achieved over 80% reduction in state space size.
Enabled sub-millisecond A* search in large indoor scenes.
Attained 100% planning success across all tested queries.
Abstract
Ground robot navigation in complex 3D environments is often hindered by geometric ambiguity, where non-traversable structures such as furniture share local geometric properties with navigable ground. Furthermore, the computational cost of searching massive voxel spaces remains a significant challenge. To address these issues, we present a surface extraction framework that constructs a reduced state space of physically reachable standing positions by enforcing ground support, overhead clearance, and seed-based connectivity constraints. Evaluation across five Matterport3D indoor scenes and three PCT benchmark scenes demonstrates over 80\% state space reduction and sub-millisecond A* search on the Matterport3D scenes, with 100\% planning success across all 300 tested queries.
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