Dynamic Vehicle Detection by Pose Estimation and Model Matching Using LiDAR Data

Dynamic vehicle detection is of great significance for perceiving the dynamic development trend of the environment and plays an important role in construction of static environment and accurate localization. To detect vehicles robustly and quickly in various scenarios without training a large amount of data, a vehicle detection approach based on a novel pose estimation method is proposed using LiDAR remotely-sensed data. The distribution characteristics of point clouds on a vehicle surface are analyzed and described by normal vector histogram (NVH) features. Considering the characteristics of vehicle surfaces, a vehicle measurement model consisting of four rectangles is proposed. Combined with the Kullback-Leibler divergence measure, the fitting degree between the point cloud clusters and a measurement model can be obtained, and the position and attitude of a candidate vehicle can be determined. With the estimated pose information, vehicle motion characteristics are utilized to determine the candidate vehicles. Finally, experiments on Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets are implemented to verify the superiority of the proposed detection approach. Particularly, in distant areas, the F₁ score is increased by 9.6 % compared with the previous pose estimation based on the coherent point drift (PE-CPD) method.