科研成果详情

Autonomous driving requires 3D representations of the environments as high definition maps. In many cases, it is not efficient for a single vehicle to map the entire large environment. Therefore, a group of vehicles could cooperate to build maps. In this paper, we propose an approach for cooperative 3D mapping by multiple vehicles working simultaneously as a team. Each vehicle uses 3D LIDAR sensor and local mapping algorithms to build local map and the global map can be obtained by merging all the local maps in an consistent manner. The challenges in cooperative mapping lie in both accuracy and efficiency. We show that our cooperative mapping approach can save mapping time as well as reduce the accumulated error often suffered by single vehicle mapping algorithms. Meanwhile, real world experiments results indicate that our mapping algorithm can be implemented online with minimum burden imposed on communication channel and computation resources on each vehicle.