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Iterative Closest Point (ICP) 〔 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. In the algorithm, one point cloud, the ''reference'', or ''target'', is kept fixed, while the other one, the ''source'', is transformed to best match the reference. The algorithm iteratively revises the transformation (combination of translation and rotation) needed to minimize the distance from the source to the reference point cloud. Inputs: reference and source point clouds, initial estimation of the transformation to align the source to the reference (optional), criteria for stopping the iterations. Output: refined transformation. Essentially, the algorithm steps are: # For each point in the source point cloud, find the closest point in the reference point cloud. # Estimate the combination of rotation and translation using a mean squared error cost function that will best align each source point to its match found in the previous step. # Transform the source points using the obtained transformation. # Iterate (re-associate the points, and so on). Zhang proposes a modified K-D tree algorithm for efficient closest point computation. 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. There exist many ICP variants, from which point-to-point and point-to-plane are the most popular. The latter usually performs better in structured environments.〔http://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf〕〔François Pomerleau, Francis Colas, Roland Siegwart, and Stéphane Magnenat. (Comparing ICP Variants on Real-World Data Sets. ) In Autonomous Robots, 34(3), pages 133–148, DOI: 10.1007/s10514-013-9327-2, April 2013.〕 ==Implementations== * (Sparse ICP ) Robust implementation of the ICP algorithm using sparse norms (both point to plane and point to point). Header only C++ (Mozilla Public License v. 2.0) * (libpointmatcher ) is an implementation of both point-to-point and point-to-plane ICP. It is released under a permissive BSD license. * (LIBICP: C++ Library for Iterative Closest Point Matching ), released under the GNU General Public License. * (ICP ) implements many variants of ICP in Matlab. It is release under the BSD license. * 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. Released under the GNU General Public License. * 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Iterative closest point」の詳細全文を読む スポンサード リンク
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