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Idea behind the iterative closest point algorithm
Iterative closest point (ICP)[1][2][3][4] is an algorithm employed to minimize the difference between two clouds of points. ICP is often used to reconstruct 2D or 3D surfaces from different scans, to localize robots and achieve optimal path planning (especially when wheel odometry is unreliable due to slippery terrain), to co-register bone models, etc.
^Arun, Somani; Thomas S. Huang; Steven D. Blostein (1987). "Least-square fitting of two 3-D point sets". IEEE Pattern Analysis and Machine Intelligence. 9 (5): 698–700. CiteSeerX 10.1.1.467.9356. doi:10.1109/TPAMI.1987.4767965. PMID 21869429. S2CID 8724100.
^Besl, Paul J.; N.D. McKay (1992). "A Method for Registration of 3-D Shapes". IEEE Transactions on Pattern Analysis and Machine Intelligence. 14 (2): 239–256. doi:10.1109/34.121791.
^Chen, Yang; Gerard Medioni (1991). "Object modelling by registration of multiple range images". Image Vision Comput. 10 (3): 145–155. doi:10.1016/0262-8856(92)90066-C.
^Cite error: The named reference zhang IJCV 1994 was invoked but never defined (see the help page).
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