Multiple Plane Fitting Algorithm to Evaluate the Accuracy of 3D Point Cloud Using Structured Light Measurement
Main Article Content
Abstract
3D shape measurement by structured light is a high-speed method and capable of profiling complex surfaces. In particular, the processing of measuring data also greatly affects the accuracy of obtained point clouds. In this paper, an algorithm to detect multiple planes on point cloud data was developed based on RANSAC algorithm to evaluate the accuracy of point cloud measured by structural light. To evaluate the accuracy of the point cloud obtained, two-step height parts are used. The planes are detected and the distance between them needs to be measured with high accuracy. Therefore, the distance measurement data between the planes found in the point cloud is compared with the data measured by CMM measurement. The experimental results have shown that the proposed algorithm can identify multiple planes at the same time with a maximum standard deviation of 0.068 (mm) and the maximum relative error is 1.46%.
Keywords
3D shape measurement, Fringe projection, fitting plane, RANSAC algorithm
Article Details
References
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[2] Standards-P. 1: M. ISO 5436-1:2000(E) Surface texture: Profile method; Measurement Measures, Iso 5436-1, vol. GPS. 2000.
[3] M. a Fischler and R. C. Bolles, Random Sample Consensus: A Paradigm for Model Fitting with Apphcatlons to Image Analysis and Automated Cartography, Commun. ACM, vol. 24, no. 6, pp. 381-395, 1981.
[4] M. Y. W. Yang and W. Forstner, Plane Detection in Point Cloud Data, Dep. Photogramm. Inst. Geod. Geoinf. Univ. Bonn, no. 1, p. 14 pp, 2010.
[5] R. Schnabel, R. Wahl, and R. Klein, Efficient RANSAC for point-cloud shape detection, Comput. Graph. Forum, vol. 26, no. 2, pp. 214-226, 2007.
[6] P. H. S. Torr and A. Zisserman, MLESAC: A new robust estimator with application to estimating image geometry, Comput. Vis. Image Underst., vol. 78, no. 1, pp. 138-156, 2000.
[7] S. Huang, Y. Liu, N. Gao, and Z. Zhang, Distance Calibration between Reference Plane and Screen in Direct Phase Measuring Deflectometry, 2018.
[8] B. J. Tordoff and D. W. Murray, Guided-MLESAC: Faster image transform estimation by using matching priors, IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1523-1535, 2005.
[9] O. Galo, R. Manduchi, and A. Rafii, CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data, Pattern Recognit. Lett., vol. 32, no. 3, pp. 403-410, 2011.
[10] A. Vedaldi, H. Tin, P. Favaro, and S. Soatto, KALMANSAC: Robust filtering by consensus, Proc. IEEE Int. Conf. Comput. Vis., vol. I, pp. 633-640, 2005.
[11] H. Wang and D. Suter, Robust adaptive-scale parametric model estimation for computer vision, IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1459-1474, 2004.
[12] L. Li, F. Yang, H. Zhu, D. Li, Y. Li, and L. Tang, An improved RANSAC for 3D point cloud plane segmentation based on normal distribution transormation cells, Remote Sens., vol. 9, no. 5, 2017.
[13] Nguyen Thi Kim Cuc, Nguyen Van Vinh, Nguyen Thanh Hung, Nguyen Viet Kien, Improving the Accuracy of the Calibration Method for Structured Light System, Journal of Science & Technology 127 (2018) 016-021.