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Iterative Closest Point Method

version 1.6.0.1 (6.68 KB) by Per Bergström
Fits a set of data points to a set of model points under a rigid body transformation

24.5K Downloads

Updated 02 Jul 2021

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The ICP (iterative closest point) algorithm finds a rigid body transformation such that a set of data points fits to a set of model points under the transformation. Default is to use least squares minimization but other criterion functions can be used as well. The implementation is based on the IRLS-ICP described in [1].
References:
[1] Bergström, P. and Edlund, O. 2014, “Robust registration of point sets using iteratively reweighted least squares”, Computational Optimization and Applications, vol 58, no. 3, pp. 543-561, doi: 10.1007/s10589-014-9643-2
[2] Bergström, P. and Edlund, O. (2016) 2017, “Robust registration of surfaces using a refined iterative closest point algorithm with a trust region approach”, Numerical Algorithms, doi: 10.1007/s11075-016-0170-3
Doi links:
http://dx.doi.org/10.1007/s10589-014-9643-2
http://dx.doi.org/10.1007/s11075-016-0170-3
http://dx.doi.org/10.1007/s00170-010-2950-6
Springer Nature’s SharedIt links (full paper online access):
http://rdcu.be/nJRM
http://rdcu.be/noHE
http://rdcu.be/nJUW
A demonstration of applications, where an ICP-algorithm [2] is implemented in Matlab and C, can be seen on YouTube
https://youtu.be/cPS-DY9sCz4

Cite As

Per Bergström (2021). Iterative Closest Point Method (https://www.mathworks.com/matlabcentral/fileexchange/12627-iterative-closest-point-method), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
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