Automatic registration of airborne LiDAR point cloud data and optical imagery depth map based on line and points features

Airborne light detection and ranging (LiDAR) technology draws increasing interest in large scale 3D urban modeling in recent years. In this paper, we propose a novel automatic registration method based on depth map using point-feature based and line-feature based registration ways to carry on the processing. First of all, the paper applies the mathematical morphology filter to preprocess point cloud data. Secondarily the depth maps of optical imagery and point cloud data are generated from adaptive support weight dense stereo matching algorithm and Delaunay triangulation algorithm, respectively. After that, point-feature based registration and line-feature based registration method are carried on. The point-feature based registration employs scale-invariant feature transform (SIFT) to generate feature correspondence between the two depth maps. Thereafter the line-feature based registration extracts line features thus to choose the angle and the length ratio of the intersecting line segments as similarity measure for coarse registration. In addition, outliers are removed with a two-level random sample consensus (RANSAC) algorithm to improve robustness and efficiency. And we can get accuracy estimation of camera position parameters. Texture and color mapping are achieved in the last step. The registration methods both can be completed automatically, and do not require GPS/INS prior knowledge. The proposed methods are efficient, which are validated by the experimental results tested with real data. (C) 2015 Elsevier B.V. All rights reserved.