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Sensors 2017, 17(9), 1979; doi:10.3390/s17091979

Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm

School of Geodesy and Geomatics, Wuhan University, Luoyu Road 129, Wuhan 430079, China
Authors to whom correspondence should be addressed.
Received: 4 July 2017 / Revised: 10 August 2017 / Accepted: 25 August 2017 / Published: 29 August 2017
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Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (TLS) point clouds (TLS-TLS point clouds) and alignment between TLS and mobile LiDAR scanning (MLS) point clouds (TLS-MLS point clouds). The scanning station position acquired by the TLS built-in GPS and the quasi-horizontal orientation of the LiDAR sensor in data acquisition are used as constraints to narrow the search space in GA. A new fitness function to evaluate the solutions for GA, named as Normalized Sum of Matching Scores, is proposed for accurate registration. Our method is divided into five steps: selection of matching points, initialization of population, transformation of matching points, calculation of fitness values, and genetic operation. The method is verified using a TLS-TLS data set and a TLS-MLS data set. The experimental results indicate that the RMSE of registration of TLS-TLS point clouds is 3~5 mm, and that of TLS-MLS point clouds is 2~4 cm. The registration integrating the existing well-known ICP with GA is further proposed to accelerate the optimization and its optimizing time decreases by about 50%. View Full-Text
Keywords: terrestrial LiDAR scanning; mobile LiDAR scanning; point cloud; registration; genetic algorithm terrestrial LiDAR scanning; mobile LiDAR scanning; point cloud; registration; genetic algorithm

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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. (CC BY 4.0).

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Yan, L.; Tan, J.; Liu, H.; Xie, H.; Chen, C. Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm. Sensors 2017, 17, 1979.

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