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Sensors 2017, 17(8), 1862; https://doi.org/10.3390/s17081862

An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features

1,* , 1,2,* , 3
,
3
and
2
1
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China
2
Department of Automation, Tsinghua University, Beijing 100084, China
3
Shenzhen Graduate School, Tsinghua University, Shenzhen 518055, China
*
Authors to whom correspondence should be addressed.
Received: 28 April 2017 / Revised: 26 July 2017 / Accepted: 29 July 2017 / Published: 11 August 2017
(This article belongs to the Section Remote Sensors)
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Abstract

The Iterative Closest Points (ICP) algorithm is the mainstream algorithm used in the process of accurate registration of 3D point cloud data. The algorithm requires a proper initial value and the approximate registration of two point clouds to prevent the algorithm from falling into local extremes, but in the actual point cloud matching process, it is difficult to ensure compliance with this requirement. In this paper, we proposed the ICP algorithm based on point cloud features (GF-ICP). This method uses the geometrical features of the point cloud to be registered, such as curvature, surface normal and point cloud density, to search for the correspondence relationships between two point clouds and introduces the geometric features into the error function to realize the accurate registration of two point clouds. The experimental results showed that the algorithm can improve the convergence speed and the interval of convergence without setting a proper initial value. View Full-Text
Keywords: ICP registration; geometric features; point clouds ICP registration; geometric features; point clouds
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He, Y.; Liang, B.; Yang, J.; Li, S.; He, J. An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features. Sensors 2017, 17, 1862.

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