The Challenging Robotics in Year-round, Outdoor, and Time-critical missions In extreme Conditions (CRYOTIC) project aims to develop resilient autonomous navigation for winter and extreme environments through three key thrusts: perception, navigation, and motion. These thrusts will advance resilient autonomous navigation for winter and extreme environments. The research will be grounded in three main applications: A1 - long-distance off-road resupply missions prioritizing goal achievement and adaptability; A2 - inspection of remote transmission lines where mobility and endurance are critical; and A3 - monitoring electrical substations requiring precise navigation in dense, fragile environments. Together, these applications form a diverse framework for testing autonomy in time, distance, and infrastructure-critical contexts. The resulting innovations will strengthen robotic perception, navigation, and motion control across varied terrains, ensuring robust and energy-efficient performance in subarctic conditions.
Quick jump to:
Objectives
- Perception in subarctic environments: Snow and blizzards challenge robot autonomy. Understanding and mitigating their effects on perception algorithms is key for reliable operation in Canadian conditions.
- Navigation under unforeseen conditions: In unstructured terrains like boreal forests, robots face obstacles such as deep snow or fallen trees. We aim to predict and recover from these events rather than avoid them.
- Field Testing and Integration: Cold climates reduce energy efficiency and control stability. Incorporating energy management into planning and control algorithms enables longer, autonomous operation in remote areas.
Reports
People
Publications
Journal Articles
- Samson, N., Larrivée-Hardy, W., Dubois, W., Roy-Brouard, É., Brotherton, E., Baril, D., Lépine, J., & Pomerleau, F. (2025). DRIVE Through the Unpredictability: From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty. IEEE Transactions on Field Robotics, 2, 380–399. https://doi.org/10.1109/TFR.2025.3580397
Publisher Bibtex source - Gamache, O., Fortin, J.-M., Boxan, M., Pomerleau, F., & Giguère, P. (2025). Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark, and Lessons Learned. IEEE Transactions on Field Robotics, 2, 270–287. https://doi.org/10.1109/TFR.2025.3566694
Publisher Bibtex source
Conference Articles
- Dubois, W., Samson, N., Daum, E., Laconte, J., & Pomerleau, F. (2025). Under Pressure: Altimeter-Aided ICP for 3D Maps Consistency. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA55743.2025.11128024
Publisher Bibtex source - Fortin, J.-M., Gamache, O., Fecteau, W., Daum, E., Larrivée-Hardy, W., Pomerleau, F., & Giguère, P. (2025). UAV-Assisted Self-Supervised Terrain Awareness for Off-Road Navigation. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA55743.2025.11128050
Publisher Bibtex source - 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 - 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 - 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