A 64-Line Lidar-Based Road Obstacle Sensing Algorithm for Intelligent Vehicles

Abstract

Based on the 64-line lidar sensor, an object detection and classification algorithm with both effectiveness and real time is proposed. Firstly, a multifeature and multilayer lidar points map is used to separate the road, obstacle, and suspension object. Then, obstacle grids are clustered by a grid-clustering algorithm with dynamic distance threshold. After that, by combining the motion state information of two adjacent frames, the clustering results are corrected. Finally, the SVM classifier is used to classify obstacles with clustered object position and attitude features. The good accuracy and real-time performance of the algorithm are proved by experiments, and it can meet the real-time requirements of the intelligent vehicles.