Efficient Registration of Laser-Scanned Point Clouds of Bridges Using Linear Features
Hiroaki Date*1,†, Takahito Yokoyama*1, Satoshi Kanai*1, Yoshiro Hada*2, Manabu Nakao*3, and Toshiya Sugawara*4
*1Graduate School of Information Science and Technology, Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan
*2Fujitsu Laboratories Ltd., Atsugi, Japan
*3Fujitsu Ltd., Kawasaki, Japan
*4Docon Co., Ltd., Sapporo, Japan
Efficient registration of point clouds from terrestrial laser scanners enables us to move from scanning to point cloud applications immediately. In this paper, a new efficient rough registration method of laser-scanned point clouds of bridges is proposed. Our method relies on straight-line edges as linear features, which often appear in many bridges. Efficient edge-line extraction and line-based registration methods are described in this paper. In our method, first, sampled regular point clouds based on the azimuth and elevation angles are created, and planar regions are extracted using the region growing on the regular point clouds. Then, straight lines of the edges of the planar regions are extracted as linear features. Next, vertical and horizontal line clusters are created according to the direction of the lines. To align the position and orientation of two point clouds, two corresponding nonparallel line pairs from line clusters are used. In the registration process, the RANSAC approach with a hash table of line pairs is used. In this process, the hash table is used for finding candidates of corresponding line pairs efficiently. Sampled points on the line pairs are used to align the line pairs, and occupied voxels and downsampled point clouds are used for efficient consensus calculation. The method is tested using three data sets of different types of bridges: a small steel bridge, a middle-size concrete bridge, and a high-pier concrete bridge. In our experiments, successful rates of our rough registration were 100%, and the processing time of rough registration for 19 point clouds was about 1 min.
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