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Laser three-dimensional (3D) image is a novel non-cooperative target 3D image acquisition technology, and the improvements in detection capability and imaging accuracy of the system are critically dependent on efficient echo-signal processing technique and 3D reconstruction method. The registration process is an essential step in array 3D imaging laser point cloud data processing. Registration of point clouds is an effective method that solves the problem caused by the target self-occlusion in the laser 3D imaging system. The accurate registration result will help provide better support for subsequent applications, such as object reconstruction and target recognition. In this study, a set of thresholds in the iterative closest point (ICP) algorithm is analysed on the basis of the characteristics of the laser array 3D imaging system and is combined with the range error and visual lateral resolution of the system, which are both important parameters in the imaging system. To improve the accuracy and speed of registration, the stop threshold of the iterative algorithm and the corresponding point-distance threshold in the algorithm are established in a novel way based on the range error and visual lateral resolution of the system. This forms the foundation, based on which an adaptive threshold ICP algorithm is proposed. The principal idea of the algorithm is to improve the threshold set that has a considerable effect on the accuracy and speed of registration. At first, the characteristics of the imaging point clouds of the laser array 3D imaging system are analysed in the algorithm. Based on this analysis, the distance between the two point clouds and corresponding points with ideal registrations are estimated theoretically, according to the range error and visual lateral resolution of the system. The simulation results show that the theoretically estimated results and actual results have the same variation tendency, thus providing a theoretical basis for subsequent improvements. Next, the estimated results are added according to the iterative closest point algorithm. This implies that the registration thresholds are capable of changing and adapting under different iterations and imaging systems, thus improving the speed and accuracy of registrations. This phenomenon is not seen in other algorithms. Experiments involving laser array imaging of a point cloud and laser scanning of depth imaging data show that the algorithm is practical and effective for both imaging types of point clouds and can improve the speed and accuracy of registration notably. The effectiveness and feasibility of the proposed algorithm are thus verified. In addition, for its full consideration of the imaging system, the basic idea of the proposed algorithm can be used for designing future applications as required.
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Keywords:
- lidar array /
- point cloud registration /
- iterative closest point algorithm /
- adaptive threshold
[1] Albota M A, Aull B F, Fouche D G, Heinrichs R M, Kocher D G, Marino R M, Mooney J G, Newbury N R, O'Brien M E, Player B E, Willard B C, Zayhowski J J 2002 Linc. Lab. J. 13 351
[2] Marino R M, Stephens T, Hatch R E, Mclaughlin J L, Mooney J G, Obrien M E, Rowe G S, Adams J S, Skelly L, Knowlton R C, Forman S E, Davis W R 2003 Proc. SPIE 5086 1
[3] Besl P J, Mckay N D 1992 IEEE Trans. Pattern Anal. Mach. Intell. 14 239
[4] Wang Y, Zhang M M, Yu X, Zhang C M 2012 Opt. Precis. Eng. 20 2068 (in Chinese)[王欣, 张明明, 于晓, 章明朝2012光学精密工程 20 2068]
[5] Jost T, Hugli H 2002 First International Symposium on 3D Data Processing Visualization and Transmission Padova, Italy, June 19-21, 2002 p540
[6] Masuda T, Sakaue K, Yokoya N 1996 Proceedings of the 13th International Conference on Pattern Recognition Washington, DC, USA, August 25-29, 1996 p879
[7] Zinsser T, Schmidt J, Niermann H 2003 2003 International Conference on Image Processing 2 695
[8] Armbruster W, Hammer M 2012 Proc. SPIE 8542 85420K
[9] Guo Y L, Wan J W, Lu M Tan Z G 2012 Opt. Precis. Eng. 20 843 (in Chinese)[郭裕兰, 万建伟, 鲁敏, 谭志国2012光学精密工程 20 843]
[10] Chen Y, Medioni G 1991 Proceedings IEEE International Conference on Robotics and Automation Sacramento, USA, April 9-11, 1991 p2724
[11] Silva L, Bellon O, Boyer K L 2015 Image Vis. Comput. 25 114
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[1] Albota M A, Aull B F, Fouche D G, Heinrichs R M, Kocher D G, Marino R M, Mooney J G, Newbury N R, O'Brien M E, Player B E, Willard B C, Zayhowski J J 2002 Linc. Lab. J. 13 351
[2] Marino R M, Stephens T, Hatch R E, Mclaughlin J L, Mooney J G, Obrien M E, Rowe G S, Adams J S, Skelly L, Knowlton R C, Forman S E, Davis W R 2003 Proc. SPIE 5086 1
[3] Besl P J, Mckay N D 1992 IEEE Trans. Pattern Anal. Mach. Intell. 14 239
[4] Wang Y, Zhang M M, Yu X, Zhang C M 2012 Opt. Precis. Eng. 20 2068 (in Chinese)[王欣, 张明明, 于晓, 章明朝2012光学精密工程 20 2068]
[5] Jost T, Hugli H 2002 First International Symposium on 3D Data Processing Visualization and Transmission Padova, Italy, June 19-21, 2002 p540
[6] Masuda T, Sakaue K, Yokoya N 1996 Proceedings of the 13th International Conference on Pattern Recognition Washington, DC, USA, August 25-29, 1996 p879
[7] Zinsser T, Schmidt J, Niermann H 2003 2003 International Conference on Image Processing 2 695
[8] Armbruster W, Hammer M 2012 Proc. SPIE 8542 85420K
[9] Guo Y L, Wan J W, Lu M Tan Z G 2012 Opt. Precis. Eng. 20 843 (in Chinese)[郭裕兰, 万建伟, 鲁敏, 谭志国2012光学精密工程 20 843]
[10] Chen Y, Medioni G 1991 Proceedings IEEE International Conference on Robotics and Automation Sacramento, USA, April 9-11, 1991 p2724
[11] Silva L, Bellon O, Boyer K L 2015 Image Vis. Comput. 25 114
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