SNOW - Self-driving Navigation Optimized for Winter

自动驾驶汽车，或称自动驾驶汽车，正在公共领域占据更大的位置，预计在不久的将来将上路。尽管在过去的十年里取得了巨大的技术进步，但冬季自动驾驶仍然引发了人们对可靠性的担忧。拉瓦尔大学拥有在机器人穿行的同时自动构建环境3D地图的专业知识，旨在利用这一知识研究与加拿大天气相关的挑战性条件的解决方案。这项提议的主要目标是将目前用于自动驾驶的技术扩展到无结构和动态的环境(例如，白雪覆盖的森林)。由UGV构建的地图将能够适应雪和风造成的环境变化，同时允许对车辆进行强大的实时定位。这张地图将作为处理可变形障碍物和环境(例如，车辆下的深雪)的新型路径规划算法的基础。该项目将与加拿大领先的防务公司通用动力陆地系统公司(GDLS-C)合作进行。该项目的成果将使拉瓦尔大学能够加强其目前的3D地图绘制算法，并在总体上在自主导航方面取得科学进展。该项目还将为GDLS-C提供新的应用机会，它将能够吸收从实验工作中获得的新知识和经验教训，为未来的客户要求做好准备

Self-driving cars, or autonomous vehicles in general, are taking a larger place in the public sphere and are expected on our roads in the near future. Although great technological advances have occurred during the last decade, autonomous driving during winter still raises reliability concerns. Laval University has the expertise to automatically build 3D maps of the environment while moving through it with robots and aims at using this knowledge to investigate solutions for challenging conditions related to Canadian weather. The main goal of this proposal is to extend the current technology used for autonomous driving toward unstructured and dynamic environments (e.g., a snow-covered forest). Maps built by a UGV will be able to adapt to environmental changes caused by snow and winds, while allowing a robust localization of the vehicle in real-time. This map will serve as the foundation for novel path planning algorithms handling deformable obstacles and environments (e.g., deep snow under a vehicle). The project will be carried out in partnership with General Dynamics Land Systems - Canada (GDLS-C), a leading Canadian defence company. The outcomes of this project will allow Laval University to strengthen its current 3D mapping algorithms and make scientific advances in autonomous navigation in general. The project will also open new application opportunities for GDLS-C, which will be able to absorb new knowledge and lessons learned from the experimental work to prepare for future customer requests