Registration of ground‐based LiDAR point clouds by means of 3D line features

Abstract

Techniques for extracting data from LiDAR point clouds can be refined for increased accuracy. In this paper, the authors elaborate on an innovative approach for registering ground-based LiDAR point clouds using overlapping scans based on 3D line features. The proposed working scheme consists of three major kernels: a 3D line feature extractor, a 3D line feature matching mechanism, and a mathematical model for simultaneously registering ground-based LiDAR point clouds of multi-scans on a 3D line feature basis. All processing chains in this study are featured efficiently and come close to meeting the needs of practical usage. Experiments conducted show the proposed method of employing 3D line features to be a useful alternative or complement to point, surface and other features for LiDAR (Light Detection And Ranging) point clouds registration. It is especially effective in areas rich in man-made structures.

Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds

“…We used error function e(x, y, z) to describe the inconsistency between the two conjugate surfaces; hence, Equation (7) can be rewritten as Equation (8). To minimize the error function e(x, y, z), the coordinate system of the MLS (x0, y0 z0) was subjected to a general 3-D translation, scaling, and rotation transformation (the so called "3-D similarity transformation") used to minimize the integrated squared error function between these two conjugate surfaces over a well-defined common spatial domain.…”

“…For the image, the control lines were extracted manually; for the LiDAR point cloud, the line features were intersected by two near planes. Jaw and Chuang [8] also proposed a line-based method to register terrestrial LiDAR point cloud scanned from different stations.…”

3-D Feature-Based Matching by RSTG Approach

“…However, reliable corresponding points have to be known in advance due to the point-to-point restriction of the transformation. For a more flexible processing, this paper employs the line-based similarity transformation model (Jaw and Chuang, 2008), which lies on trajectory-based restriction to get a closed-form solution of transformation parameters. The collinear property can be described by:…”

Registration between Multiple Laser Scanner Data Sets

“…Dold & Brenner(2006) and Pathak et al(2010) brought geometric constraints into the matching of plane matches of the plane based registration method, and Makadia et al(2006) proposed an extended Gaussian image based method to estimate the rotation automatically. Jaw & Chuang(2007) introduced a framework to integrate point, line and plane features together, and compared the integrated method with algorithms using such features separately, he found that the integrated method is much more stable than those separated ones. Rabbani et al(2007) goes even far more, he integrated the modeling and registration together, simultaneously determined the shape and pose parameters of the objects as well as the registration parameters.…”