In order to detect and track the target on road

an algorithm of target detection and tracking based on multi-layer lidar is proposed. Collected points cloud data is filtered out using ground plane fitting algorithm

the filtered points are rasterized and clustered by region growing algorithm. The information of targets is obtained from bounding box（ BBox） constructed by random sample consensus（ RANSAC） algorithm and build corresponding tracker by improved Hungarian algorithm. Updating the state of the target using the Kalman filtering algorithm. The improved Hungarian algorithm can solve the problem of non-equilibrium target assignment and adapt to the sudden increase or decrease of targets in the environment. The reliability of the algorithm is demonstrated by the KITTI data set.