Beyond Geometry: Efficient Topologically-Grounded Navigation in Complex 3D Environments

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.