1 citations · 1 across the 2 of their papers we have counts for
8 papers
Extrinsic calibration for highly accurate trajectories reconstruction
Maxime Vaidis, William Dubois, Alexandre Guénette +3
In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long d…
On the Importance of Quantifying Visibility for Autonomous Vehicles under Extreme Precipitation
Clément Courcelle, Dominic Baril, François Pomerleau +1
In the context of autonomous driving, vehicles are inherently bound to encounter more extreme weather during which public safety must be ensured. As climate is quickly changing, th…
Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments
Johann Laconte, Elie Randriamiarintsoa, Abderrahim Kasmi +4
In the context of autonomous vehicles, one of the most crucial tasks is to estimate the risk of the undertaken action. While navigating in complex urban environments, the Bayesian…
Improving the Iterative Closest Point Algorithm using Lie Algebra
Maxime Vaidis, Johann Laconte, Vladimír Kubelka +1
Mapping algorithms that rely on registering point clouds inevitably suffer from local drift, both in localization and in the built map. Applications that require accurate maps, suc…
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
Dominic Baril, Vincent Grondin, Simon-Pierre Deschênes +6
In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control…
Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment
Johann Laconte, Christophe Debain, Roland Chapuis +2
In a context of autonomous robots, one of the most important task is to ensure the safety of the robot and its surrounding. Most of the time, the risk of navigation is simply said…