Motion measurement system can simultaneously collect three-dimensional (3D) laser point clouds, image texture, and the position and attitude data; however the registration fusion of image photos and laser point cloud is challenging to meet the needs of high efficiency and high overlap. This paper proposes a method to register video images with laser point clouds under motion measurement to solve this problem. The positioning orientation system (POS) sensor’s position and attitude data are used to calculate the initial registration value of keyframes in a video image and a laser point cloud using a collinear equation model. The iterative method for selecting weights based on Robust estimation improves the accuracy of initial registration values and determines the exact values of registration parameters. The stereo-dense matching point cloud is generated from the keyframe of the video image, and the nearest neighbor iterative registration is performed using the 3D laser point cloud. The experimental results show that the registration method of video image and a vehicular laser point cloud is feasible, and the registration accuracy is high, meeting the requirements of 3D reconstruction of urban streets, component acquisition, target extraction, and other measurement applications.