针对单一传感器在动态场景感知问题上的局限性，设计了一种融合激光与视觉的实现系统，并对运动检测中的背景显露区误判问题和融合中不同传感器间点云的失配问题分别提出了改进算法。在运动检测上，首先基于视觉的背景差分算法对激光进行前景点分拣，再以激光前景点为启发信息进行视觉前景聚类。在融合失配问题上，首先基于栅格失配度分别对激光和视觉点云进行聚类分割，再以激光为基准，逐一将对应的视觉点云与之配准，滤除噪声后所得到的矫正点云可用于场景重建进行进一步验证。实验结果表明，改进算法所获得的融合前景对“影子”有更好的鲁棒性；较之整体配准的矫正，改进算法在平均失配度上降低了约75%，在y和z方向上的偏移比收敛了至少5%。

Aiming at the limitations of single sensor in dynamic scene perception issue, an implementation sys-tem 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 re-constructing 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%.