Fast Registration of Terrestrial LiDAR Point Clouds Based on Gaussian-Weighting Projected Image Matching

Abstract

Terrestrial point cloud registration plays an important role in 3D reconstruction, heritage restoration and topographic mapping, etc. Unfortunately, current research studies heavily rely on matching the 3D features of overlapped areas between point clouds, which is error-prone and time-consuming. To this end, we propose an automatic point cloud registration method based on Gaussian-weighting projected image matching, which can quickly and robustly register multi-station terrestrial point clouds. Firstly, the point cloud is regularized into a 2D grid, and the point density of each cell in the grid is normalized by our Gaussian-weighting function. A grayscale image is subsequently generated by shifting and scaling the x-y coordinates of the grid to the image coordinates. Secondly, the scale-invariant features (SIFT) algorithm is used to perform image matching, and a line segment endpoint verification method is proposed to filter out negative matches. Thirdly, the transformation matrix between point clouds from two adjacent stations is calculated based on reliable image matching. Finally, a global least-square optimization is conducted to align multi-station point clouds and then obtain a complete model. To test the performance of our framework, we carry out the experiment on six datasets. Compared to previous work, our method achieves the state-of-the-art performance on both efficiency and accuracy. In terms of efficiency, our method is comparable to an existing projection-based methods and 4 times faster on the indoor datasets and 10 times faster on the outdoor datasets than 4PCS-based methods. In terms of accuracy, our framework is ~2 times better than the existing projection-based method and 6 times better than 4PCS-based methods.