REGISTRATION OF MMS LiDAR POINTS AND PANORAMIC IMAGE SEQUENCE USING RELATIVE ORIENTATION MODEL

Abstract

Abstract. We propose using the relative orientation model (ROM) of panoramic to register the MMS LiDAR points and panoramic image sequence, which has the wide applicability. The feature points, extracted and matched from panoramic image pairs, are used to solve the relative position and attitude parameters in the ROM, then, combining the absolute position and attitude parameters of the initial panoramic image, the MMS LiDAR points and panoramic image sequence are registered. First, we propose the position/attitude ROM (PA-ROM) and attitude ROM (A-ROM) of panoramic images respectively, which are apply to the position/attitude parameters both unknown and only the attitude parameters unknown. Second, we automatically extract and match feature points from panoramic image pairs using the SURF algorithm, as these mismatching points will affect the registration accuracy, the RANSAC algorithm and ROM were used to choose the best matching points automatically. Finally, we select the feature points manually from MMS LiDAR points and panoramic image sequence as the checkpoints, and then compare the registration accuracy of continuous/discontinuous panoramic image pairs. The results show that MMS LiDAR points and panoramic image sequence are registered accurately based on ROM (7.36 and 3.75 pixels in dataset I and II), what's more, our registration method just tackle the image pairs (uninvolved LiDAR points), so it is suitable for more road scenes.

Automatic Registration of Panoramic Image and Point Cloud Based on the Shape of the Overall Ground Object

“…To achieve this, data registration is required, which involves calculating a transformation matrix that converts the two sets of data into the same coordinate system and eliminates any geometric inconsistencies. As point clouds and panoramic images are cross-modal data, existing registration methods mostly rely on the initial EOPs provided by GPS/IMU [7], which unfortunately results in poor registration accuracy. For the 2D-3D registration problem, scholars have proposed many solutions.…”