Topological Mapping for Traversability-Aware Long-Range Navigation in Off-Road Terrain
Jean-François Tremblay, Julie Alhosh, Louis Petit, Faraz Lotfi, Lara Landauro, David Meger
Abstract
Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been studied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> forest sites unseen during training, in difficult conditions for navigation.
BibTeX
@inproceedings{icra2025_topologicalmappi,
title = {Topological Mapping for Traversability-Aware Long-Range Navigation in Off-Road Terrain},
author = {Jean-François Tremblay and Julie Alhosh and Louis Petit and Faraz Lotfi and Lara Landauro and David Meger},
booktitle = {ICRA 2025},
year = {2025}
}