Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1083-1090, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1083-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1083-1090, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1083-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

MULTIPLE TLS POINT CLOUD REGISTRATION BASED ON POINT PROJECTION IMAGES

T. Sumi, H. Date, and S. Kanai T. Sumi et al.
  • Graduate School of Information Science and Technology, Hokkaido University, 060-0814 Sapporo, Japan

Keywords: Terrestrial Laser Scanning, Point Clouds, Registration, Point Projection Image, Scan Graph, Iterative Closest Point

Abstract. In this paper, an efficient and robust registration method of multiple point clouds is proposed. In our research, we assume that point clouds are acquired by Terrestrial Laser Scanning (TLS) systems, and the scanned environments have a relatively flat base plane such as the ground or a floor. Our method is based on an existing pairwise registration method based on point projection images, which can quickly register the point clouds under the above assumptions. In the method, sliced point clouds are projected onto the base plane, and a binary image with feature points is created. The registration is done by using feature points of the images based on the sample consensus strategy. In this paper, first, we improve the efficiency of the pairwise registration method by introducing height and occlusion information to the image. Then, a validity check method of pairwise registration using space-classified images is proposed to avoid exhaustive pairwise registration in the multiple point cloud registration process. Finally, an efficient multiple point cloud registration algorithm based on progressive creation of a point cloud connectivity graph using iterative rough and precise pairwise registration and the validity check method is proposed. The effectiveness of our method is shown through its application to three datasets of outdoor environments.