Portrait of Louis Petit

Louis Petit

Affiliate Member
Assistant Professor, Université de Sherbrooke, Department of Electrical and Computer Engineering
Research Topics
Autonomous Robotics Navigation
Computer Vision
Deep Learning
Multi-Agent Systems
Optimization
Reinforcement Learning
Robotics
Website
Google Scholar
LinkedIn

Biography

Louis Petit is an Assistant Professor at Université de Sherbrooke, where he leads the SAFiR Lab.

His research focuses on innovative strategies in perception, mapping, planning, decision-making and control to improve the autonomy of aerial, terrestrial and aquatic robots in real and complex environments, with applications in search and rescue, advanced driver assistance systems, infrastructure inspection, and ecosystem monitoring.

Before joining Sherbrooke, he was a postdoctoral researcher at McGill University. He holds a Ph.D. from Université de Sherbrooke, as well as a Master’s degree in Mechatronics Engineering from UCLouvain in Belgium. Prof. Petit has contributed to widely used open-source robotics libraries for motion planning and mapping.

Current Students

William Bonilla
PhD - McGill University
Co-supervisor :
David Meger
william.bonilla@mila.quebec
Github

Publications

FLINT: Fast Lightweight Inference for Traversability
William Bonilla
Maxime Boisvert
David-Alexandre Poissant
David Meger
Louis Petit
Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The tra… (see more)versability depends on both the environment and the embodiment's dynamics. Neither of these two variables can be hand-labeled at scale. Thus, traversability has to be learned by the embodiment's own experience. Modern platforms tend to use multiple sensors to estimate traversability and navigate: RGBD cameras, lidar, radar, IMU, with computationally intensive platforms to run inference on neural networks. Against this trend, we propose FLINT, a lightweight traversability estimator: a 21.6M-parameter backbone, 38\times smaller than a comparable foundation-model backbone, that scores higher on held-out terrain probes and runs at 14.7 FPS on CPU alone using a RGB camera has the only sensor. Despite that gap in scale, FLINT produces a cheaper, more accurate costmap than a deployed foundation-model system (WildOS) on 23 of 24 replayed field logs. We compare different self-supervised learning signals and deploy the resulting models on a real platform in closed-loop field trials: the best self-supervised head reaches 99% autonomy over the route, outperforming a human-label-trained baseline deployed live on the same course. Our results show that heavy sensing and computing are not necessary for traversability estimation.
2026-09-21
arXiv (preprint)
doi.org
arxiv.org
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… (see more)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 m2 forest sites unseen during training, in difficult conditions for navigation.
2025-05-18
2025 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org