Three-dimensional point cloud registration based on ICP algorithm employing K-D tree optimization

Jiang Liu; Jiwen Zhu; Jinling Yang; Xianglai Meng; He Zhang

doi:10.1117/12.2248362

29 August 2016 Three-dimensional point cloud registration based on ICP algorithm employing K-D tree optimization

Jiang Liu, Jiwen Zhu, Jinling Yang, Xianglai Meng, He Zhang

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100334D (2016) https://doi.org/10.1117/12.2248362
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

In order to improve the precision and speed of the three-dimensional point cloud registration, it is suggested the three-dimensional point cloud registration based on ICP algorithm employing k-d tree optimization in this paper. First of all, the centre superposition method is adopted to realize the point cloud coarse registration, and then improve the traditional ICP where the K-D tree is used to quickly search the closest pair of points to enhance the speed of the point cloud registration. Finally the Three dimensional point cloud coarse registration is completed precisely. The method overcomes the defects of the traditional ICP algorithm using Euclidean distance to determine the closest pair of points which is time-consuming and plains lots of work. On the basis of this method, the experiment can be verified through different density Bunny Stanford point cloud data. The result shows that using K-d tree optimization of ICP algorithm, the precision, speed and stability of the point cloud registration is improved when the centre superposition method is adopted to realize the three dimensional point cloud coarse registration.

Citation Download Citation

Jiang Liu, Jiwen Zhu, Jinling Yang, Xianglai Meng, and He Zhang "Three-dimensional point cloud registration based on ICP algorithm employing K-D tree optimization", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334D (29 August 2016); https://doi.org/10.1117/12.2248362

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