SNOW

SNOW

Self-driving Navigation Optimized for Winter

Le projet Self-driving Navigation Optimized for Winter (SNOW) a permis à une équipe robotique de l’Université Laval de développer une meilleure solution de navigation pour un véhicule terrestre autonome (UGV) dans des environnements non-structurés et dynamiques générés par les conditions hivernales. Les multiples types de précipitations en hiver perturbent les algorithmes de conduite autonome et augmentent le risque lié au déploiement de cette technologie sur les routes canadiennes. Ce projet a abordé le manque de connaissances entourant l’interaction entre les conditions hivernales et les véhicules autonomes, ainsi que la faible quantité d’expertise disponible pour soutenir les décideurs politiques.

Accès rapide :

Rapports Équipe Publications

Objectifs

  1. Cartographie et localisation : développer des algorithmes permettant au véhicule UGV de se localiser et de cartographier son environnement dans des conditions hivernales.
  2. Planification de trajectoire et contrôle : développer des algorithmes de planification de trajectoires et adapter le comportement du UGV selon les conditions météorologiques, ainsi que la réalité du terrain et la rigidité des obstacles.
  3. Tests sur le terrain et intégration : réaliser une grande variété d’expériences avec l’UGV dans une forêt enneigée. Ces déploiements sur le terrain servent de validation technique avant l’intégration de modules visant à améliorer l’autonomie du véhicule.

Rapports

Médias

Équipe

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Publications

Journal Articles

  1. 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
  2. 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
  3. Kasmi, A., Laconte, J., Aufrère, R., Denis, D., & Chapuis, R. (2020). End-to-end probabilistic ego-vehicle localization framework. IEEE Transactions on Intelligent Vehicles.
     Bibtex source
  4. Kubelka, V., Dandurand, P., Babin, P., Giguère, P., & Pomerleau, F. (2020). Radio propagation models for differential GNSS based on dense point clouds. Journal of Field Robotics. https://doi.org/10.1002/rob.21988
     Publisher    Bibtex source
  5. Labussière, M., Laconte, J., & Pomerleau, F. (2020). Geometry Preserving Sampling Method based on Spectral Decomposition for Large-scale Environments. Frontiers in Robotics and AI – Multi-Robot Systems, 7, 134. https://doi.org/10.3389/frobt.2020.572054
     Publisher    Bibtex source

Conference Articles

  1. LaRocque, D., Guimont-Martin, W., Duclos, D.-A., Giguère, P., & Pomerleau, F. (2024). Proprioception Is All You Need: Terrain Classification for Boreal Forests. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 11686–11693. https://doi.org/10.1109/iros58592.2024.10801407
     PDF    Slides    Publisher    Bibtex source
  2. Gamache, O., Fortin, J.-M., Boxan, M., Vaidis, M., Pomerleau, F., & Giguère, P. (2024). Exposing the Unseen: Exposure Time Emulation for Offline Benchmarking of Vision Algorithms. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 11110–11117. https://doi.org/10.1109/IROS58592.2024.10803057
     Publisher    Bibtex source
  3. Deschênes, S.-P., Baril, D., Boxan, M., Laconte, J., Giguère, P., & Pomerleau, F. (2024). Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/https://doi.org/10.48550/arXiv.2310.07844
     PDF    Publisher    Bibtex source
  4. Baril, D., Deschênes, S.-P., Coupal, L., Goffin, C., Lépine, J., Giguère, P., & Pomerleau, F. (2024). DRIVE: Data-driven Robot Input Vector Exploration. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA57147.2024.10611172
     Publisher    Bibtex source
  5. 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
  6. Laconte, J., Randriamiarintsoa, E., Kasmi, A., Pomerleau, F., Chapuis, R., Debain, C., & Aufrère, R. (2021). Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS).
     Bibtex source
  7. Deschênes, S.-P., Baril, D., Kubelka, V., Giguère, P., & Pomerleau, F. (2021). Lidar Scan Registration Robust to Extreme Motions. 2021 18th Conference on Robots and Vision (CRV). https://doi.org/10.1109/CRV52889.2021.00014
     PDF    Publisher    Bibtex source
  8. Kasmi, A., Laconte, J., Aufrère, R., Theodose, R., Denis, D., & Chapuis, R. (2020). An Information Driven Approach For Ego-Lane Detection Using Lidar And OpenStreetMap. 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 522–528.
     Bibtex source
  9. 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
  10. Dandurand, P., Babin, P., Kubelka, V., Giguère, P., & Pomerleau, F. (2019). Predicting GNSS satellite visibility from dense point clouds. Proceedings of the Conference on Field and Service Robotics (FSR). Springer Tracts in Advanced Robotics.
     PDF    Bibtex source
  11. Pradalier, C., Aravecchia, S., & Pomerleau, F. (2019). Multi-session lake-shore monitoring in visually challenging conditions. Proceedings of the Conference on Field and Service Robotics (FSR). Springer Tracts in Advanced Robotics.
     Bibtex source
  12. Landry, D., Pomerleau, F., & Giguère, P. (2019). CELLO-3D: Estimating the Covariance of ICP in the Real World. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
     PDF    Bibtex source
  13. Babin, P., Giguère, P., & Pomerleau, F. (2019). Analysis of Robust Functions for Registration Algorithms. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA.2019.8793791
     PDF    Publisher    Bibtex source
  14. Babin, P., Dandurand, P., Kubelka, V., Giguère, P., & Pomerleau, F. (2019). Large-scale 3D Mapping of Subarctic Forests. Proceedings of the Conference on Field and Service Robotics (FSR). Springer Tracts in Advanced Robotics.
     PDF    Slides    Bibtex source
  15. Laconte, J., Debain, C., Chapuis, R., Pomerleau, F., & Aufrere, R. (2019). Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS).
     Bibtex source

Miscellaneous

  1. Dubois, W., Boxan, M., Laconte, J., & Pomerleau, F. (2024). 3D Mapping of Glacier Moulins: Challenges and lessons learned. In presented to the 2024 Workshop on Field Robotics from IEEE International Conference on Robotics and Automation (ICRA). https://arxiv.org/abs/2404.18790
     Publisher    Bibtex source
  2. Gamache, O., Fortin, J.-M., Boxan, M., Pomerleau, F., & Giguère, P. (2024). Field Report on a Wearable and Versatile Solution for Field Acquisition and Exploration. In presented to the 2024 Workshop on Field Robotics from IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.48550/arXiv.2405.00199
     Publisher    Bibtex source
  3. Daum, E., Vaidis, M., & Pomerleau, F. (2023). Benchmarking ground truth trajectories with robotic total stations. In presented at IROS23, Workshop on Methods for Objective Comparison of Results in Intelligent Robotics Research. https://arxiv.org/abs/2309.05134 Best paper award!
     Publisher    Bibtex source
  4. Deschênes, S.-P., Baril, D., Kubelka, V., Giguère, P., & Pomerleau, F. (2023). Lidar Scan Registration Robust to Extreme Motions. Colloque REPARTI, Université Laval.
     Bibtex source
  5. Boxan, M., & Pomerleau, F. (2023). 3D Point Clouds Reconstruction of Environment Subject to Thin Structures. Colloque REPARTI, Université Laval.
     Bibtex source

inbook

  1. Courcelle, C., Baril, D., Pomerleau, F., & Laconte, J. (2023). On the Importance of Quantifying Visibility for Autonomous Vehicles under Extreme Precipitation. In H. Abut, G. Schmidt, K. Takeda, J. Lambert, & J. H. L. Hansen (Eds.), Towards Human-Vehicle Harmonization (Vol. 3, pp. 239–250). De Gruyter. https://doi.org/doi:10.1515/9783110981223-018
     Publisher    Bibtex source
  2. Pomerleau, F. (2023). Robotics in Snow and Ice. In M. H. Ang, O. Khatib, & B. Siciliano (Eds.), Encyclopedia of Robotics (pp. 1–6). Springer Berlin Heidelberg.
     PDF    Bibtex source