Automatic registration of terrestrial point clouds based on panoramic reflectance images and efficient BaySAC

Zhizhong Kang

doi:10.1117/12.2032255

26 October 2013 Automatic registration of terrestrial point clouds based on panoramic reflectance images and efficient BaySAC

Zhizhong Kang

Author Affiliations +

Proceedings Volume 8917, MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis; 891715 (2013) https://doi.org/10.1117/12.2032255
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

This paper presents a new approach to automatic registration of terrestrial laser scanning (TLS) point clouds utilizing a novel robust estimation method by an efficient BaySAC (BAYes SAmpling Consensus). The proposed method directly generates reflectance images from 3D point clouds, and then using SIFT algorithm extracts keypoints to identify corresponding image points. The 3D corresponding points, from which transformation parameters between point clouds are computed, are acquired by mapping the 2D ones onto the point cloud. To remove false accepted correspondences, we implement a conditional sampling method to select the n data points with the highest inlier probabilities as a hypothesis set and update the inlier probabilities of each data point using simplified Bayes’ rule for the purpose of improving the computation efficiency. The prior probability is estimated by the verification of the distance invariance between correspondences. The proposed approach is tested on four data sets acquired by three different scanners. The results show that, comparing with the performance of RANSAC, BaySAC leads to less iterations and cheaper computation cost when the hypothesis set is contaminated with more outliers. The registration results also indicate that, the proposed algorithm can achieve high registration accuracy on all experimental datasets.

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Zhizhong Kang "Automatic registration of terrestrial point clouds based on panoramic reflectance images and efficient BaySAC", Proc. SPIE 8917, MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis, 891715 (26 October 2013); https://doi.org/10.1117/12.2032255

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KEYWORDS

Clouds

Data modeling

Image registration

Reflectivity

Error analysis

Laser scanners

Panoramic photography

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