Autonomous Vehicle Navigation In Rural Environments Without Detailed Prior Maps
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)(2018)
摘要
State-of-the-art autonomous driving systems rely heavily on detailed and highly accurate prior maps. However, outside of small urban areas, it is very challenging to build, store, and transmit detailed maps since the spatial scales are so large. Furthermore, maintaining detailed maps of large rural areas can be impracticable due to the rapid rate at which these environments can change. This is a significant limitation for the widespread applicability of autonomous driving technology, which has the potential for an incredibly positive societal impact. In this paper, we address the problem of autonomous navigation in rural environments through a novel mapless driving framework that combines sparse topological maps for global navigation with a sensor-based perception system for local navigation. First, a local navigation goal within the sensor view of the vehicle is chosen as a waypoint leading towards the global goal. Next, the local perception system generates a feasible trajectory in the vehicle frame to reach the waypoint while abiding by the rules of the road for the segment being traversed. These trajectories are updated to remain in the local frame using the vehicle's odometry and the associated uncertainty based on the least-squares residual and a recursive filtering approach, which allows the vehicle to navigate road networks reliably, and at high speed, without detailed prior maps. We demonstrate the performance of the system on a full-scale autonomous vehicle navigating in a challenging rural environment and benchmark the system on a large amount of collected data.
更多查看译文
关键词
prior maps,positive societal impact,autonomous vehicle navigating,recursive filtering approach,navigate road networks,least-squares residual approach,vehicle frame,sensor-based perception system,global navigation,sparse topological maps,mapless driving framework,autonomous navigation,autonomous driving technology,transmit detailed maps,urban areas,rural environments,autonomous vehicle navigation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络