ComputingComputer ScienceAlgorithmsComputational GeometryComputer VisionGeometric OptimizationGeometry ProcessingPoint Cloud ProcessingSurface ReconstructionCorrespondence ProblemIterative Closest PointPoint Cloud MatchingShape Correspondence
This paper describes PLICP, an ICP (iterative closest/corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual correspondences), and MBICP (Metric-Based ICP) by reproducing the experiments performed in Minguez et al. (2006). The experiments suggest that PLICP is more precise, and requires less iterations. However, it is less robust to very large initial displacement errors. The last part of the paper is devoted to purely algorithmic optimization of the correspondence search; this allows for a significant speed-up of the computation. The source code is available for download.
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Efficient variants of the ICP algorithm
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