ICP algorithm and its variants

iterative Closest Point (ICP) ^{[1][2][3] is a algorithm employed to minimize the difference between} and clouds of points.

taxonomy (1)• Global matching algorithm globe Local matching algorithm (2) • Point-based matching • Feature-based matching • Point feature VPF FHPF ... based on line feature • "Algorithms for Matching 3D lines sets." • "Line segment-based approach for accuracy assessment of the MLS point clouds in urban areas." Poreba, M. and F. Goulette (2015). "A Robust linear feature-based procedure for automated registration of point clouds."Sensors (Basel) (1): 1435-1457. improved ICP algorithm• Speed search efficiency k-d tree Voronoi chart • Different distance measurement methods • Point-to-line PLICP Censi, A. (2008). "An ICP variant using a point-to-line metric." IEEE International Conference on Robotics & Automation. IEEE,: 19-25. CSM (Canonical Scan Matcher) source      http://censi.mit.edu/software/csm/point to polygon Low, K.-l. (2004).   ICP algorithm solution closed form SVD unit quaternions Unit four Yuan the ICP error function minimization via orthonormal matrices Dual quaternions Numerical solution LM algorithm (Levenberg-marquardt algorithm) Jerbi?, B., et al. (2015). "Robot assisted 3D point Cloud Object registration." Procedia Engineering 100:847-852. point to face linear least squares low, K.-l. (2004). "Linear least-squares optimization for Point-to-plane ICP Surface registration." Idc

The IDC algorithm does a point-to-point correspondence for calculating the scan alignment. The correspondence problem is solved by a heuristics:the closest point rule and the matching range rule. Furthermore, a formula is provided for calculating an error covariance matrix of the scan matching

The standard ICP algorithm is the first proposed algorithm based on point-to-point distance, and the other is based on the point-to-plane algorithm, which is presented by Chen, a lot of references to the bad Chen S method.

The standard ICP algorithm needs to be coarse-grained to meet the conditions close enough for accurate registration.

Because of the existence of outliner, i.e. the existence of observation error and outliers, and some overlapping problems, there is still an unstable problem (robust problem) in the process of finishing the data after coarse matching, so a robust ICP method is proposed. such as SICP

The general ICP algorithm (above) is a local optimization algorithm, there is a problem of global optimization, that is, do not need a separate coarse, direct one step. Many of the ICP algorithms are robust methods, but they are not global optimization methods. The global method has super4pcs, three points ransac and so on.

Http://www.mathworks.com/matlabcentral/fileexchange/12627-iterative-closest-point-method

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Http://projects.asl.ethz.ch/datasets/doku.php?id=laserregistration:laserregistration

ICP algorithm and its variants