Virtual road networks for long-term visual navigation of unmanned ground vehicles

移动机器人技术在过去十年中快速发展，使自动化采矿、农业、安全与安保以及自动驾驶汽车等应用成为近期现实。多伦多大学(UofT)一直在为无人地面车辆(UGV)开发一种视觉路线跟踪技术，这将成为实现这一愿景的关键构件。到目前为止，该技术主要是在短时间段(即几天)的点对点路径上进行测试。 该提议的主要目标是扩展这一核心技术，以允许定义整个虚拟道路网络(例如，数十公里的互连路径)，并将其用于在长时间段(即，数月或数年)的各种服务应用中的UGV的可视导航。这将需要在几个领域取得重大进展，包括地图维护、强大的本地化和动态路线规划。该项目将与ClearPath Robotics Inc.合作进行，ClearPath Robotics Inc.是一家位于加拿大的新兴世界级无人驾驶车辆提供商。 导航技术的这一飞跃将使UofT在移动机器人长期自主性领域取得核心科学进步，并将使ClearPath将这些进步融入他们未来的产品和解决方案，从而扩大他们的客户基础。

Mobile robotics technology has advanced at a rapid pace in the last decade, making applications such as automated mining, agriculture, safety & security, and self-driving cars a near-term reality. The University of Toronto (UofT) has been developing a visual route-following technique for Unmanned Ground Vehicles (UGVs) that will serve as a key building block to enable this vision. To date, the technique has been tested primarily on point-to-point paths over short periods of time (i.e., a few days). The main goal of this proposal is to extend this core technique to allow an entire virtual road network (e.g., tens of kilometres of interconnected paths) to be defined and used for visual navigation of UGVs in a variety of service applications over long periods of time (i.e., months or years). This will require major advances in several areas including map maintenance, robust localization, and dynamic route planning. The project will be carried out in partnership with Clearpath Robotics Inc., an emerging world-class provider of unmanned vehicles located in Canada. This leap forwards in navigation technology will allow UofT to make core scientific advances in the area of long-term autonomy for mobile robots and will allow Clearpath to incorporate these advances in their future products and solutions, thereby expanding their customer base.