Efficient Estimation of 3D Shifts between Point Clouds Using Low-Frequency Components of Phase Correlation

Registration of multiple point clouds acquired via terrestrial laser scanning (TLS) is usually compulsory to obtain the scanned data covering a whole urban scene. However, the automated processing of aligning multiple scans is still a concern because of the complex urban environment. To this end, we propose a fast and sturdy estimation of 3D shifts between point clouds by an automated markerfree process using global features, converting translation measurement between two point clouds in the space domain to the frequency domain and estimating the phase difference. By using the low-frequency components from the normalized cross-power spectrum, accurate 3D shifts are calculated by solving parameters in the linear equation representing phase difference angles, with the help of a robust estimator. The results of experiments using TLS datasets of different scenes show that the proposed approach is both practical and efficient. In particular, the proposed approach can achieve results with a translation error of less than about 1.0 m on test datasets.