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Point cloud matching using singular value decomposition

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Abstract

We propose a simple, fast three-dimensional (3D) matching method that determines the best rotation matrix between non-corresponding point clouds (PCs) with no iterations. An estimated rotation matrix can be derived by the two following steps: (1) the singular value decomposition is applied to a measured data matrix, and a database matrix is constructed from the PC datasets; (2) the inner product of each left singular vector is used to produce the estimated rotation. Through experimentation, we demonstrate that the proposed method executes 3D PC matching with <4 % of the computational time of the iterative closest point algorithm with nearly identical accuracy.

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Authors and Affiliations

  1. Kyushu Institute of Technology, Fukuoka, Japan

    Shinji Oomori, Takeshi Nishida & Shuichi Kurogi

Authors
  1. Shinji Oomori
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  2. Takeshi Nishida
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  3. Shuichi Kurogi
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Correspondence to Shinji Oomori.

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Oomori, S., Nishida, T. & Kurogi, S. Point cloud matching using singular value decomposition. Artif Life Robotics 21, 149–154 (2016). https://doi.org/10.1007/s10015-016-0265-x

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  • DOI: https://doi.org/10.1007/s10015-016-0265-x

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