Obstacle detection and tracking algorithm based on multi‐lidar fusion in urban environment

Abstract

Adjacent obstacles are difficult to be distinguished, and remote obstacle is easily detected as multiple targets. Besides, occluded obstacles are difficult to track, and tracking velocity of the obstacles in rapid acceleration or deceleration converges slowly in the urban environment. In view of this, an obstacle detection and tracking method based on multi-lidar is proposed. Firstly, based on the vehicle kinematics model, motion compensation is adopted to solve the space-time synchronization problem among lidars after road segmentation, and data level fusion is completed. Next, the obstacles are detected by combining adaptive voxel grid DBSCAN (AVG-DBSCAN) algorithm and Region Growing (RG) algorithm. Then, the Breadth First Search (BFS) algorithm and KD tree are adopted to improve the Evolutionary Hungarian algorithm for the fast association of obstacles. Finally, the occlusion rate and dynamic region selection are used to track obstacles accurately, based on the Kalman Filter of uniform acceleration model. The experimental results on the authors' extracted urban dataset show that the proposed method can effectively fuse multi-lidar data and outperforms other methods in obstacle detection and tracking. Its average accuracy for detection is 97.53% and its average accuracy for tracking is 95.1%. The average duration of the entire process is only 30 ms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

RF-LOAM: Robust and Fast LiDAR Odometry and Mapping in Urban Dynamic Environment

“…All tests are performed in C++ based on the Robot Operating System (ROS) and run on a computer with i7-8700 CPU, 16 GB RAM and Ubuntu 18.04. Then, RF-LOAM uses the detailed constant parameters shown in Table I, according to reference [42] and practical experience.…”