Julie Alhosh

juliealhosh@gmail.com

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e

David Meger

Sujets de recherche

Apprentissage par renforcement

Apprentissage profond

Site web

Google Scholar

GitHub

Publications

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

Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (voir plus)ied, 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 meters squared forest sites unseen during training, in difficult conditions for navigation.

2025-05-19

2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)

doi.org

arxiv.org

Tractable Representations for Convergent Approximation of Distributional HJB Equations

Julie Alhosh

Harley Wiltzer

David Meger

2025-03-07

ArXiv (prépublication)

arxiv.org