Different Scale Point Clouds Alignment Using Dominant Plane

Abstract

The search activities to Great East Japan Earthquake are periodically done by many volunteers. Although their search area is extremely wide, the activities are done by human wave tactics. To make such activities more efficient, it is important that analysis of drone video for generating 3-D point cloud from a scene. If we can detect the different places between two point clouds obtained on different days, the detected places have high possibilities for missing objects. In this paper, we proposes a new method for aligning 3-D point clouds using dominant plane information. Our method uses a new keypoint-based feature descriptor for an almost planar surface point cloud and estimate a scale using the dominant plane in the point cloud. Our method can be applied to different scale point clouds. We show the effectiveness of our method by several experiments using simulated data from actually measured point clouds.