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Remote Sens. 2013, 5(12), 6921-6937; doi:10.3390/rs5126921
Article

Block-to-Point Fine Registration in Terrestrial Laser Scanning

Received: 23 October 2013; in revised form: 3 December 2013 / Accepted: 5 December 2013 / Published: 11 December 2013
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Abstract: Fine registration of point clouds plays an important role in data analysis in Terrestrial Laser Scanning (TLS). This work proposes a block-to-point fine registration approach to correct the errors of point clouds from TLS and of geodetic networks observed using total stations. Based on a reference coordinate system, the block-to-point estimation is performed to obtain representative points. Then, fine registration with a six-parameter transformation is performed with the help of an Iterative Closest Point (ICP) method. For comparisons, fine registration with a seven-parameter transformation is introduced by applying a Singular Value Decomposition (SVD) algorithm. The proposed method not only corrects the registration errors between a geodetic network and the scans, but also considers the errors among the scans. The proposed method was tested on real TLS data of a dam surface, and the results showed that distance discrepancies of estimated representative points between scans were reduced by approximately 60%.
Keywords: terrestrial laser scanning; fine registration; block-to-point estimation; systematic errors terrestrial laser scanning; fine registration; block-to-point estimation; systematic errors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Wang, J. Block-to-Point Fine Registration in Terrestrial Laser Scanning. Remote Sens. 2013, 5, 6921-6937.

AMA Style

Wang J. Block-to-Point Fine Registration in Terrestrial Laser Scanning. Remote Sensing. 2013; 5(12):6921-6937.

Chicago/Turabian Style

Wang, Jin. 2013. "Block-to-Point Fine Registration in Terrestrial Laser Scanning." Remote Sens. 5, no. 12: 6921-6937.


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