Welcome to the site of the Northern Robotics Laboratory (norlab) at Laval University. Our research laboratory is specialized in mobile and autonomous systems working in northern or difficult conditions. We aim at investigating new challenges related to navigation algorithms to push the boundary of what is currently possible to achieve with a mobile robot in real-life conditions. The current focus of the laboratory is on localization algorithms designed for laser sensors (lidar) and 3D reconstruction of the environment. We will use this website to showcase our results and to simplify the knowledge transfer with some general tips & tricks.
Norlab goes to CRV!
This month, the lab attended the 17th conference on Computer and Robot Vision, which was held virtually due to the COVID-19 situation.
Our first group photo!
It has been a good year in term of team building and equipment acquisition. Next year will most probably reserve us a lot of surprises, but I’m certain that...
Norlab is going to Asia!
We are going to Tokyo (Japan) and Macau (China) with four accepted publications at the 2019 International Conference on Field and Service Robotics and one at...
First appearance of the lab in IEEE Spectrum
Norlab appears in the online version of the magazine IEEE Spectrum with the title DARPA Subterranean Challenge: Meet the First 9 Teams. They confuse our Fren...
Self-driving cars are expected on our roads soon. In the project SNOW (Self-driving Navigation Optimized for Winter), we focus on the unexplored problem of a...
DARPA - SUBTERRANEAN CHALLENGE
DARPA - Subterranean Challenge (DARPA-SubT) is an international robotics competition focusing on autonomy, perception, networking, mobility technologies, and...
The Montmorency dataset
This large scale forest mapping dataset in now available for download on Academic Torrents. The dataset contains the ground truth species, diameter at breast...
Exploration of large caves is challenging using traditional mapping tools. There are limited places to install a tripod equipped with a highly accurate lidar...
Improving the Iterative Closest Point Algorithm using Lie Algebra
Mapping algorithms that rely on registering point clouds inevitably suffer from local drift, both in localization and in the built map. Applications that req...
Geometry Preserving Sampling Method based on Spectral Decomposition for Large-scale Environments
In the context of 3D mapping, larger and larger point clouds are acquired with lidar sensors. Although pleasing to the eye, dense maps are not necessarily ta...
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State esti...
Large-scale 3D Mapping of Subarctic Forests
The ability to map challenging subarctic environments opens new horizons for robotic deployments in industries such as forestry, surveillance, and open-pit m...