Efficient Registration of Laser-Scanned Point Clouds of Bridges Using Linear Features

Abstract

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.

An Improved Coherent Point Drift Method for TLS Point Cloud Registration of Complex Scenes

“…However, feature point based methods are sensitive to outliers or point density variations. Apart from these, straight lines [Date et al, 2018] and regular planes [Forstner et al, 2017] are also popular primitives, but limited to artificial environments where regular features can be easily extracted. Besides, spatial curves [Yang et al, 2014] and curved planes [Raposo et al, 2018] are frequently used as registration primitives as well, exhibiting good performance for free-form objects.…”

Template-Guided Hierarchical Multi-View Registration Framework of Unordered Bridge Terrestrial Laser Scanning Data

“…Although some researchers have studied registration methods of bridge PCD, they mainly focused on pairwise registration [ 8 , 9 ]. However, to align multiple scans of a bridge to a unified coordinate system, the utilization of multi-view registration techniques is essential.…”

Point Cloud Target Registration Algorithm Based on Local Feature Description

“…The 2D image of the target obtained by the traditional visible-light imaging technology can only provide a small amount of target information [1], which is difficult to meet the needs in many cases, while the point cloud data obtained by the 3D imaging technology based on laser scanning can provide more target information, such as spatial coordinates, color, intensity et al [2][3][4]. so the target recognition problem based on point cloud data has received extensive attention [5][6][7].…”