Iterative closest point
Iterative Closest Point (ICP) [1][2][3] 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.
The algorithm is conceptually simple and is commonly used in real-time. It iteratively revises the transformation (translation, rotation) needed to minimize the distance between the points of two raw scans.
Inputs: points from two raw scans, initial estimation of the transformation, criteria for stopping the iteration.
Output: refined transformation.
Essentially the algorithm steps are :
- Associate points by the nearest neighbor criteria[clarification needed].
- Estimate transformation parameters using a mean square cost function.
- Transform the points using the estimated parameters.
- Iterate (re-associate the points and so on).
In,[3] a modified K-D tree algorithm is proposed for efficient closest point computation, and a statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which enables subset-subset matching.
See also
- LIBICP: C++ Library for Iterative Closest Point Matching
- MeshLab an open source mesh processing tool that includes a GNU General Public License implementation of the ICP algorithm.
- CloudCompare an open source point and model processing tool that includes an implementation of the ICP algorithm.
- PCL (Point Cloud Library) is an open-source framework for n-dimensional point clouds and 3D geometry processing. It includes several variants of the ICP algorithm.
- Open source C++ implementations of the ICP algorithm are available in VTK and ITK libraries.
References
- ^ Besl, Paul J. (1992). "A Method for Registration of 3-D Shapes". IEEE Trans. on Pattern Analysis and Machine Intelligence. 14 (2). Los Alamitos, CA, USA: IEEE Computer Society: 239–256. doi:10.1109/34.121791.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Chen, Yang (1991). "Object modelling by registration of multiple range images". Image Vision Comput. Newton, MA, USA: Butterworth-Heinemann: 145–155. doi:10.1016/0262-8856(92)90066-C.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ a b Zhang, Zhengyou (1994). "Iterative point matching for registration of free-form curves and surfaces". International Journal of Computer Vision. 13 (12). Springer: 119–152. doi:10.1007/BF01427149.
External links
- Iterative cage-based registration for dynamic shape capture (2012) (Yann Savoye)
- Iterative Point Matching for Registration of Free-Form Curves and Surfaces (1992) (Zhengyou Zhang)
- Derivation of ICP Equations
- Efficient Variants of the ICP Algorithm
- Finite Iterative Closest Point Method Matlab
- Iterative Closest Point Method in Matlab
- Iterative Closest Point Method in C++
- Iterative Closest Point algorithm in VTK
- Iterative Closest Point implementation in C++
- A modular Iterative Closest Point implementation in C++
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