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. 2017 Aug 29;17(9):1979.
doi: 10.3390/s17091979.

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

Li Yan  1 , Junxiang Tan  2 , Hua Liu  3 , Hong Xie  4 , Changjun Chen  5
Affiliations

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

Li Yan et al. Sensors (Basel). .

Abstract

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%.

Keywords: genetic algorithm; mobile LiDAR scanning; point cloud; registration; terrestrial LiDAR scanning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The framework and selection of matching points of the proposed GA registration. The GA evolution is showed in the dashed box. (a) The flow chart of GA registration; (b) The process of selection of mathing points.
Figure 2
Figure 2
Scattered points removal. The point clouds are rendered by elevation. (a) The point cloud before scattered points removal; (b) the point cloud after scattered points removal.
Figure 3
Figure 3
Normal space sampling. The point clouds are rendered by elevation. (a) The point cloud before normal space sampling; (b) the point cloud after 5% points were selected; (c) the point cloud after 0.1% points were selected.
Figure 4
Figure 4
The score function and its instances. (a) The diagram of score function; (b) the instances of score function under different distance thresholds.
Figure 5
Figure 5
The test datasets. The red data are target point clouds and the blue data are source point clouds. (a) The raw point clouds of data set 1; (b) the raw point clouds of data set 2; (c) the point clouds of data set 1 after selection of matching points; (d) the point clouds of data set 2 after selection of matching points; (e) the registered point clouds of data set 1; (f) the registered point clouds of data set 2.
Figure 6
Figure 6
The spherical targets of data set 1. (a) the used target (white sphere). (b) the distribution of the targets (red circle points). The red triangle denotes the scan station.
Figure 7
Figure 7
The rough matching for dataset 2 by software Cloud Compare.
Figure 8
Figure 8
The failure rates, RMSEs and mean optimizing time of different sampling ratios. (a–c) are the results of data set 1. (d–f) are the results of data set 2.
Figure 9
Figure 9
The examples of the situations where the registration goes wrong. The red data are target point clouds and the blue data are source point clouds. (a) The mismathed point clouds of data set 1; (b) the mismathed point clouds of data set 2; (c) a detail-zoom of (a); (d) a detail-zoom of (b).
Figure 10
Figure 10
The mean and maximum fitness values of GA. A polyline with random color represents the change of the fitness value of one experiment. (a) The change of the fitness values of data set 1; (b) the change of the fitness values of data set 2.
Figure 11
Figure 11
The mean number of generations and optimizing time of GA registration and GA + ICP.
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