Moving Objects Detection in Evidential Occupancy Grids Using Laser Radar

With the increase of traffic jams, the intelligent vehicle gains more and more attention. They exploit exteroceptive sensors to have an accurate perception of the surroundings in the urban environment, like a multi-layer laser radar or camera. This paper presents an occupancy grid framework to handle uncertainty sources caused by laser radar. The inverse sensor model in Cartesian coordinate is used to transform the sensor data into occupancy grid map. Two grids are used, local grid map which transforms the current sensor data and global grid map performing the temporal integration of data in a fixed frame based on Dempster's rule of combination and PCR2. The conflict information is used to detect the moving objects, the expansion algorithm, erosion algorithm and the priority marking algorithm based on regional growing are used for the cluster analysis, and box models are established for the moving objects. The outdoor experimental results show that such a perception strategy can steadily and accurately detect the moving objects.