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Iterative curved surface fitting algorithm using a raster-scanning window

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Abstract

In this paper, an iterative curved surface fitting method using a small sliding window is first proposed to smooth the original organized point cloud data (PCD) with noise and fluctuation. Samples included in a small sliding window positioned in PCD are successively fitted to a quadratic surface from upper left to lower right using a least squares method. In the iterative process, outliers of samples are asymptotically removed based on an evaluation index. This proposed method allows original PCD to be smoothed keeping its own shape feature. Then, the already developed stereolithography (STL) generator is used to produce triangulated patches from the smoothed PCD. The process allows to reconstruct 3D digital data of a real object written with STL format for reverse engineering from original PCD with noise. The effectiveness and usefulness of the proposed curved surface fitting method are demonstrated through actual smoothing experiments.

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References

  1. He X, Ni M, Xue Y, Lu Y, Li C (2010) An algorithm for topology reconstruction of scattered point cloud in reverse engineering. In: Proceedings of 2010 8th world congress on intelligent control and automation (WCICA2010), Jinan, China, pp 3126–3131

  2. Muslimin, Yoshioka H, Zhu J, Tanaka T (2016) Automatic segmentation and feature identification of laser scanning point cloud data for reverse engineering. In: Proceedings of 2016 international symposium on flexible automation (ISFA2016), Cleveland, OH, USA, pp 278–285

  3. Xu H, Yao X, Tian Y (2006) Filtering of scattered 3D data points in reverse engineering. In: Proceedings of 2006 international conference on machine learning and cybernetics (ICMLC2006), Dalian, China, pp 1471–1476

  4. Mallick T, Das PP, Majumdar AK (2014) Characterizations of noise in Kinect depth images: a review. IEEE Sens J 14(6):1731–1740

    Article  Google Scholar 

  5. Nagata F, Takeshita K, Watanabe K (2016) Smoothing of PCD using a small sliding window and generation of STL data for smart robotic machining process. In: Proceedings of the society of instrument and control engineers annual conference (SICE2016), Tsukuba, Japan, pp 1169–1172

  6. Nagata F, Horie N, Ochi H, Watanabe K, Habib MK (2017) Curved surface fitting method using a raster-scanning window and its application to stereolithography-based reverse engineering. In: Proceedings of the 43th annual conference of the IEEE industrial electronics society (IECON2017), Beijing, China, pp 6258–6263

  7. Nagata F, Takeshita K, Watanabe K, Habib MK (2016) Generation of triangulated patches smoothed from original point cloud data with noise and its application to robotic machining. In: Proceedings of the 2016 IEEE international conference on mechatronics and automation (ICMA 2016), Shangri-La Hotel, Harbin, China, pp 535–540

  8. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by JSPS KAKENHI Grant number JP16K06203.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Sanyo-onoda City University, 1-1-1 Daigaku-Dori, Sanyo-Onoda, 756-0884, Japan

    Fusaomi Nagata, Akimasa Otsuka, Takeshi Ikeda & Hiroaki Ochi

  2. Okayama University, Okayama, Japan

    Keigo Watanabe

  3. American University in Cairo, New Cairo, Egypt

    Maki K. Habib

Authors
  1. Fusaomi Nagata
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  2. Akimasa Otsuka
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  3. Takeshi Ikeda
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  4. Hiroaki Ochi
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  5. Keigo Watanabe
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  6. Maki K. Habib
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Corresponding author

Correspondence to Fusaomi Nagata.

Additional information

This work was presented in part at the 23rd International Symposium on Artificial Life and Robotics, Beppu, Oita, January 18–20, 2018.

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Nagata, F., Otsuka, A., Ikeda, T. et al. Iterative curved surface fitting algorithm using a raster-scanning window. Artif Life Robotics 23, 359–366 (2018). https://doi.org/10.1007/s10015-018-0444-z

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  • DOI: https://doi.org/10.1007/s10015-018-0444-z

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