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Effective criterion for evaluating registration accuracy

정합 정밀도 판단을 위한 효과적인 기준

  • Received : 2021.03.25
  • Accepted : 2021.04.12
  • Published : 2021.05.31

Abstract

When acquiring a point cloud using a 3D scanner, a registration process of making the acquired data based on each local coordinate into one data with a unified world coordinate system is required. Its process is difficult to obtain a satisfactory result with only one execution, and it is repeated several times to increase the registration precision. The criterion for determining the registration accuracy is an important factor. The previous methods for determining the accuracy of registration have a limitation in that the judgment may be ambiguous in some cases, and different results may be produced each time depending on the characteristics of the point cloud. Therefore, to calculate the accuracy of registration more precisely, I propose a method using the average distance value of the point group for the entire points rather than the corresponding points used in the registration. When this method is used, it is possible to determine the registration accuracy more reliably than the conventional methods.

3D 스캐너를 이용하여 점군을 획득 시 각각의 고유한 좌표를 기준으로 취득한 데이터를 통일된 좌표체계를 가진 하나의 데이터로 만드는 과정이 필요하고 이 과정을 정합이라고 한다. 정합 과정은 한 번의 수행으로 만족할만한 결과를 얻기 힘들며 여러 차례 반복하여 정합 정밀도를 높인다. 정합의 정밀도를 판단하는 기준은 중요한 요소이다. 기존에 정합의 정밀도를 파악하는 방법은 경우에 따라 판단 기준이 모호할 수 있으며, 점군 데이터의 특성에 따라 매번 다른 결과가 나올 수 있는 한계점을 가지고 있다. 이에 본 연구에서는 정합의 정밀도는 좀 더 정확하게 계산하기 위하여 정합에서 사용하는 대응점이 아닌 전체 점군에 대해서 점군의 평균 거리 값을 이용한 방법을 제안한다. 이 방법을 사용할 경우 기존의 방법에 비하여 좀 더 확실하게 정합의 정밀도를 파악할 수 있다.

Keywords

References

  1. F. Deng, "Registration between multiple laser scanner data sets," in Laser Scanning, Theory and Applications, pp. 449-472, 2011.
  2. K. Y. Kown, "A weighted points registration method to analyze dimensional errors occurring during shipbuilding process," Transactions of the Society of CAD/CAM Engineers, vol. 21, no. 2, pp. 151-158, 2016. https://doi.org/10.7315/CADCAM.2016.151
  3. J. Zhang, Y. Yao, and B. Deng, "Fast and Robust Iterative Closest Point," IEEE Transactions on Pattern Analysis and Machine Intelligence, preprint, 2021.
  4. B. Becerik-Gerber, F. Jazizadeh, G. Kavulya, and G. Calis, "Assessment of target types and layouts in 3D laser scanning for registration accuracy," Automation in Construction, vol. 20, no. 5, pp. 649-658, 2011. https://doi.org/10.1016/j.autcon.2010.12.008
  5. P. J. Besl and M. D. McKay, "Method for registration of 3-D shapes," International Society for Optics and Photonics, vol. 1611, pp. 586-607, 1992.
  6. M. He, L. Huang, B. Zhao, B. Chen, and B. Hu, "Advanced functional materials in solid phase extraction for ICP-MS determination of trace elements and their species - A review," Analytica Chimica Acta, vol. 973, no. 22, pp. 1-24, 2017. https://doi.org/10.1016/j.aca.2017.03.047
  7. Z. Wu, H. Chen, S. Du, M. Fu, N. Zhou, and N. Zheng, "Correntropy based scale ICP algorithm for robust point set registration," Pattern Recognition, vol. 93, pp. 14-24, 2019. https://doi.org/10.1016/j.patcog.2019.03.013
  8. D. H. Yun, S. I. Choi, S. H. Kim, and K. H. Ko, "Registration of multiview point clouds for application to ship fabrication," Graphical Models, vol. 90, pp. 1-12, 2017. https://doi.org/10.1016/j.gmod.2017.02.001
  9. S. Rusinkiewicz, "A Symmetric Objective Function for ICP," ACM Transactions on Graphics, vol. 38, no. 4, pp. 1-7, 2019. https://doi.org/10.1145/3306346.3323037
  10. N. Mellado, D. Aiger, and N. J. Mitra, "Fast global pointcloud registration via smart indexing," Computer Graphics Forum, vol. 33, no. 5, pp. 205-215, 2014. https://doi.org/10.1111/cgf.12446
  11. T. H. Cho, "Efficient CUDA Implementation of Multiple Planes Fitting Using RANSAC," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 4, pp. 388-393, 2019. https://doi.org/10.6109/JKIICE.2019.23.4.388
  12. Y. He, B. Liang, J. Yang, S. Li, and J. He, "An iterative closest points algorithm for registration of 3D laser scanner point clouds with geometric features," Sensors, vol. 17, no. 8, 2017.
  13. Stanford University 3D Scan Repository [Internet]. Available: http://graphics.stanford.edu/data/.