Maxime Vaidis

Maxime Vaidis

Started on
vaidis.maxime@gmail.com

Expertise: Robotic total stations, GNSS, underground mapping, Iterative Closest Point, registration

Follow me:
Google Scholar Google Scholar
Research Gate Research Gate
Linkedin Linkedin
ORCID ORCID

Maxime Vaidis is currently a Ph.D candidate 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 using several robotic total stations to obtain the six degrees of freedom of a robotic platform.

Education

  • 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

Projects involved

  • Project RTS (main work)
  • DARPA competitions (Urban and Final challenges respectively in 2020 and 2021)
  • Project Scutigera
  • Project SNOW

Publications

Journal Articles

  1. Rouček, T., Pecka, M., Čížek, P., Petříček, T., Bayer, J., Šalanský, V., Azayev, T., Heřt, D., Petrlík, M., Báča, T., Spurný, V., Krátký, V., Petráček, P., Baril, D., Vaidis, M., Kubelka, V., Pomerleau, F., Faigl, J., Zimmermann, K., … Krajník, T. (2022). System for multi-robotic exploration of underground environments CTU-CRAS-NORLAB in the DARPA Subterranean Challenge. Field Robotics, 2(1), 1779–1818. https://doi.org/10.55417/fr.2022055
     Publisher  Bibtex source
  2. Baril, D., Deschênes, S.-P., Gamache, O., Vaidis, M., LaRocque, D., Laconte, J., Kubelka, V., Giguère, P., & Pomerleau, F. (2022). Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned. Field Robotics, 2(1), 1628–1660. https://doi.org/10.55417/fr.2022050
     PDF Publisher  Bibtex source
  3. Chahine, G., Vaidis, M., Pomerleau, F., & Pradalier, C. (2021). Mapping in unstructured natural environment: A sensor fusion framework for wearable sensor suites. SN Applied Sciences, 3(5).
     Bibtex source

Conference Articles

  1. Vaidis, M., Dubois, W., Daum, E., LaRocque, D., & Pomerleau, F. (2023). Uncertainty analysis for accurate reference trajectories with robotic total stations. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS). Accepted for oral presentation
     Bibtex source
  2. Vaidis, M., Dubois, W., Guénette, A., Laconte, J., Kubelka, V., & Pomerleau, F. (2023). Extrinsic calibration for highly accurate trajectories reconstruction. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://arxiv.org/abs/2210.01048
     Publisher  Bibtex source
  3. Kubelka, V., Vaidis, M., & Pomerleau, F. (2022). Gravity-constrained point cloud registration. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 4873–4879. https://doi.org/10.1109/IROS47612.2022.9981916
     Publisher  Bibtex source
  4. 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
     PDF Publisher  Bibtex source
  5. 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 Best Robotic Vision Paper Award!
     PDF Publisher  Bibtex source

Miscellaneous

  1. Vaidis, M., Dubois, W., Guénette, A., Laconte, J., Kubelka, V., & Pomerleau, F. (2023). Extrinsic calibration for highly accurate trajectories reconstruction. Colloque REPARTI, Université Laval.
     Bibtex source
  2. 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. https://doi.org/10.1109/CRV52889.2021.00012
     Bibtex source