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Research Papers

Improvements to the Iterative Closest Point Algorithm for Shape Registration in Manufacturing

[+] Author and Article Information
Tsz-Ho Kwok

Department of Mechanical and
Aerospace Engineering,
The Hong Kong University of
Science and Technology,
Clear Water Bay, Hong Kong
e-mail: tom.thkwok@gmail.com

Kai Tang

Department of Mechanical and
Aerospace Engineering,
The Hong Kong University of
Science and Technology,
Clear Water Bay, Hong Kong

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received November 1, 2014; final manuscript received August 7, 2015; published online September 9, 2015. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 138(1), 011014 (Sep 09, 2015) (7 pages) Paper No: MANU-14-1575; doi: 10.1115/1.4031335 History: Received November 01, 2014; Revised August 07, 2015

Iterative closest point (ICP) is a popular algorithm used for shape registration while conducting inspection during a production process. A crucial key to the success of the ICP is the choice of point selection method. While point selection can be customized for a particular application using its prior knowledge, normal-space sampling (NSS) is commonly used when normal vectors are available. Normal-based approach can be further improved by stability analysis—called covariance sampling. The stability analysis should be accurate to ensure the correctness of covariance sampling. In this paper, we go deep into the details of covariance sampling, and propose a few improvements for stability analysis. We theoretically and experimentally show that these improvements are necessary for further success in covariance sampling. Experimental results show that the proposed method is more efficient and robust for the ICP algorithm.

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Figures

Grahic Jump Location
Fig. 1

The contact surfaces of these mechanical parts are not completely constrained, i.e., they have certain DOF

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Fig. 2

These two parts are mechanically constrained (left), where their contact surface (right) is analyzed. (Top-right) Before optimizing the rotation center position (located at theintersection of the three rotation axes), the stability of the y-axis is reported as high. (Bottom-right) After updating the rotation center, the analysis gives a much lower stability for they-axis.

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Fig. 3

Two examples of point extraction: a cube (top) and a blade (bottom). From left to right: the input with the candidate points, and the extracted points in three cases with differentstabilities of 40.0, 20.0, and 10.0 for cube; 8.0, 4.0, 2.0 for blade.

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Fig. 4

Comparison between the original covariance sampling and the improved one proposed in this paper. Three examples are shown in this figure, and each of them shows a chart of RMS alignment error against the iteration steps. From the rabbit and rocker arm examples, we can see that the improved version converges more than two times faster than the original one. In the chair example, even though both methods have the same minimum stability, the original method fails to converge while the improved one does.

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Fig. 5

Comparisons between our method and the HKS method, the NSS method, and the 3D-SIFT method. In the tests on both objects, our method performs much better than the rest.

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Fig. 6

Three range surfaces capture different portions of a teapot, and their data are overlapped only partially

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Fig. 7

Comparison between different levels of Gaussian noise is added in terms of transformation error. The amount of noise is related to the percentage of the model's bounding ball radius.

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