Abstract
Registration of point clouds is a central problem in many mapping and monitoring applications, such as outdoor and indoor mapping, high-speed railway track inspection, heritage documentation, building information modeling, and others. However, ensuring the global consistency of the registration is still a challenging task when there are multiple point clouds because the different scans should be transformed into a common coordinate frame. The aim of this paper is the registration of multiple terrestrial laser scanner point clouds. We present a plane-based matching algorithm to find plane-to-plane correspondences using a new parametrization based on complex numbers. The multiplication of complex numbers is based on analysis of the quadrants to avoid the ambiguity in the calculation of the rotation angle formed between normal vectors of adjacent planes. As a matching step may contain several matrix operations, our strategy is applied to reduce the number of mathematical operations. We also design a novel method for global refinement of terrestrial laser scanner data based on plane-to-plane correspondences. The rotation parameters are globally refined using operations of quaternion multiplication, while the translation parameters are refined using the parameters of planes. The global refinement is done non-iteratively. The experimental results show that the proposed plane-based matching algorithm efficiently finds plane correspondences in partial overlapping scans providing approximate values for the global registration, and indicate that an accuracy better than 8 cm can be achieved by using our global fine plane-to-plane registration method.
Highlights
In recent years, the rising role of building information modeling (BIM) has driven the market for static terrestrial laser scanners (TLS)
The plane matching method is evaluated in terms of matching success rate (SR), by counting the number of correctly matched planes over all pairs of point clouds and the matching time (MT)
Its performance is compared to the K-4PCS registration algorithm [6], which is available in the point cloud library (PCL) [39]
Summary
The rising role of building information modeling (BIM) has driven the market for static terrestrial laser scanners (TLS). To derive globally consistent 3D point cloud models from TLS with high positional accuracy, registration is a mandatory task. Pairwise registration using free-form correspondences (e.g., iterative closest point algorithm [1]), feature point-based (e.g., keypoints) methods [2,3,4,5,6,7], or primitive-based (e.g., lines or planar surfaces) approaches [8,9,10,11,12,13] should be applied first to obtain the transformation parameters. A problem which arises in the registration of multiple point clouds is that corresponding scan features may still present significant residual errors from pairwise registration task. To the best of our knowledge, the proposed method is the first using this approach
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