Maxime Vaidis is currently a Ph.D student in Norlab laboratory. He got a formation in mathematics and physics of two years in the preparatory classes for the Grandes Ecoles at Faidherbe (Lille, France) as well as two Master’s degrees, one in electrical Engineering from UQAC (University of Quebec At Chicoutimi, Canada), and an other one in Telecommunication and computer Engineering from Telecom Saint Etienne (University of Jean Monnet-Saint-Etienne, France). He did an internship at UQAC in LAIMI laboratory (Automatic and 3D Multimodal Intelligent Interaction Laboratory), during which he participated in the develoment of a smart insole and a creation of a new type of robot’s swarm and his interaction which a human operator. He also worked for ConformiT compagny through a research contrat to develop a new accident-prevention system working with some Deep learning. His current works are about to develop a robot which can map some caves with Lidar sensors.
- Master’s degree in electrical Engineering at UQAC (University of Quebec At Chicoutimi, Canada), 2016-2018
- Master’s degree in Telecommunication and computer Engineering from Telecom Saint Etienne (University of Jean Monnet-Saint-Etienne, France), 2014-2018
- Preparatory classes for the Grandes Ecoles in Mathematics and Physics at Faidherbe (Lille, France), 2012-2014
- Chahine, G., Vaidis, M., Pomerleau, F., & Pradalier, C. (2020). Mapping in unstructured natural environment: A sensor fusion framework for wearable sensor suites. SN Applied Sciences.
- Vaidis, M., Giguère, P., Pomerleau, F., & Kubelka, V. (2021). Accurate outdoor ground truth based on total stations. 2021 18th Conference on Robots and Vision (CRV). https://arxiv.org/abs/2104.14396
Publisher Bibtex source
- Baril, D., Grondin, V., Deschenes, S., Laconte, J., Vaidis, M., Kubelka, V., Gallant, A., Giguere, P., & Pomerleau, F. (2020). Evaluation of Skid-Steering Kinematic Models for Subarctic Environments. 2020 17th Conference on Computer and Robot Vision (CRV), 198–205. https://doi.org/10.1109/CRV50864.2020.00034
PDF Publisher Bibtex source
- Vaidis, M., Laconte, J., Kubelka, V., & Pomerleau, F. (2020). Improving the Iterative Closest Point Algorithm using Lie Algebra. In IROS 2020 Workshop - Bringing geometric methods to robot learning, optimization and control.