Association-Matrix-Based Sample Consensus Approach for Automated Registration of Terrestrial Laser Scans Using Linear Features

This paper presents an approach for the automatic registration of terrestrial laser scans using linear features. The main contribution here is introducing a new matching strategy that uses an association matrix to store information about candidate matches of linear features. The motivation for this work is aiding the 3D modeling of industrial sites rich with pole-like features. The proposed matching strategy aims at establishing hypotheses about potential minimal matches of linear features that could be used for the estimation of the transformation parameters relating the scans; then, quantifying the agreement between the scans using the estimated transformation parameters. We combine the association matrix and the well-known RANSAC approach for the derivation of conjugate pairs among the two scans. Rather than randomly selecting the line pairs as in the RANSAC-based registration, the association matrix guides the process of selecting the candidate matches of linear features. Experiments are conducted using laser scanning data of an electrical substation to assess the performance of the proposed association-matrix-based sample consensus approach as it compares to the traditional RANSAC-based procedure. The association-matrix-based approach showed consistent tendency of bringing up the correct matches first before the RANSAC-based registration.