Motion detection and mismatching correction by fusing laser and vision

Abstract

Aiming at the limitations of single sensor in dynamic scene perception issue, an implementation system for fusing laser and vision was designed. In addition, two improved algorithms were proposed to solve the problems of the error foreground detection in the motion detection and the mismatching between the point clouds of different sensors. As for motion detection, the laser foreground points were firstly detected based on visual background subtraction algorithm. Then, the visual foreground was clustered regarding laser foreground points as the heuristic information. To solve the mismatching of fusion, the laser and vision point cloud were segmented into clusters based on the cell mismatching degree firstly. Then the corresponding stereo point cloud was registered referring to laser clusters. The corrected point cloud could be used for further verification by reconstructing the scene after filtering. The experimental results showed that the fusion foreground obtained finally had a better robustness to shadow. Compared with the whole registration correction, the average mismatching degree reduced by 75%, and the positive ratio in the direction of y and z converged at least 5%.