Implicit vehicle positioning with cooperative lidar sensing

This paper considers the problem of cooperative localization of passive objects in a vehicular environment through the fusion of lidar point clouds collected at different moving vehicles and sent to the road infrastructure. Object localization is then used to improve the position estimate of vehicles according to the implicit cooperative positioning paradigm. At first, each vehicle uses a deep neural network (a 3D object detector) to process its lidar point cloud and localize static objects. Then, the set of estimated bounding boxes is sent to the road infrastructure, which performs data association through a message passing neural network to identify the set of measurements originating from the same detected object. Lastly, cooperative localization of objects is backward used to improve vehicle positioning. Simulations of a realistic cooperative lidar sensing scenario with CARLA software highlight improved positioning compared to non-cooperative tracking.