A Modified Sequential Monte Carlo Bayesian Occupancy Filter Using Linear Opinion Pool for Grid Mapping

Abstract

Occupancy grid state mapping is a key process in robotics and autonomous driving systems. It divides the environment into grid cells that contain information states. In this paper, we propose a modified SMC-BOF method to map and predict occupancy grids. The original SMC-BOF has been widely used in the occupancy grid mapping because it has lower computational costs than the BOF method. However, there are some issues related to conflicting information in dynamic situations. The original SMC-BOF cannot completely control an elongated vehicle that has conflicting information caused by the height difference between backward of vehicle and ground. To overcome this problem, we add confidence weights onto a part of the grid mapping process of the original SMC-BOF using the Linear Opinion Pool. We evaluate our method by LIDAR and stereo vision data in the KITTI dataset.