Bienvenue sur le site du Laboratoire de robotique boréale (norlab) de l'Université Laval. Notre laboratoire de recherche est spécialisé en systèmes mobiles et autonomes fonctionnant en conditions nordiques ou difficiles en général. Nous visons l'étude de problèmes reliés au algorithmes de navigation dans l'optique de repousser les limites de ce qui est présentement possible de faire avec un robot mobile en conditions d'utilisations réelles. Présentement, nos efforts sont dirigés vers les algorithmes de localisation basés sur les capteurs laser (lidar) et sur la reconstruction 3D d'environnements. Nous utiliserons ce site web afin de présenter nos résultats de recherche et de simplifier le transfert de connaissances grâce à des trucs et conseils généraux.
Nouvelles
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Norlab goes to CRV!
(Seulement en anglais) This month, the lab attended the 17th conference on Computer and Robot Vision, which was held virtually due to the COVID-19 situation.
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Our first group photo!
(Seulement en anglais) 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...
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Norlab is going to Asia!
(Seulement en anglais) 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...
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First appearance of the lab in IEEE Spectrum
(Seulement en anglais) 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...
Recherche
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Project SNOW
(Seulement en anglais) 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...
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DARPA - SUBTERRANEAN CHALLENGE
(Seulement en anglais) DARPA - Subterranean Challenge (DARPA-SubT) is an international robotics competition focusing on autonomy, perception, networking, mobility technologies, and...
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The Montmorency dataset
(Seulement en anglais) This large scale forest mapping dataset in now available for download on Academic Torrents. The dataset contains the ground truth species, diameter at breast...
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Projet Scutigera et cartographie souterraine
L’exploration de larges cavernes en utilisant les outils classiques de scan 3D est toujours un veritable défi. L’espace est assez limité pour permettre l’ins...
Publications
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Improving the Iterative Closest Point Algorithm using Lie Algebra
(Seulement en anglais) Mapping algorithms that rely on registering point clouds inevitably suffer from local drift, both in localization and in the built map. Applications that req...
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Geometry Preserving Sampling Method based on Spectral Decomposition for Large-scale Environments
(Seulement en anglais) 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...
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Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
(Seulement en anglais) 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...
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Large-scale 3D Mapping of Subarctic Forests
(Seulement en anglais) The ability to map challenging subarctic environments opens new horizons for robotic deployments in industries such as forestry, surveillance, and open-pit m...