Iterative closest point

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.

The transformation (combination of translation and rotation) is iteratively estimated in order to minimize an error metric, typically the sum of squared differences between the coordinates of the matched pairs.

ICP is one of the widely used algorithms in aligning three dimensional models given an initial guess of the rigid transformation required.

[5] The ICP algorithm was first introduced by Chen and Medioni,[3] and Besl and McKay.

In this work a statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance, and disappearance, which enables subset-subset matching.