Iterative Closest Point Method

Fits a set of data points to a set of model points under a rigid body transformation

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