A Parallelized Iterative Closest Point Algorithm for 3D View Fusion
The Iterative Closest Point Algorithm (ICP) is a widely used method in computer science and robotics, used for minimizing a distance metric between two set of points. Common applications of the ICP are object localization and position estimation. In this work, we introduce a parallel version of the ICP which significantly reduces the computational time, by performing fewer operations while maintaining a simple and highly parallelizable algorithm. Our proposal is based on the naive computation of closest pairs of points in two different sets, instead of comparing all possible pairs we approximate the closest pairs of points by means of searching in a plausible subset. The experiments are performed on a sample from the Stanford 3D Scanning Repository, used for the 3D cloud of points registration. For these case studies, the error, as well as the solution, are exactly the same than using the exact algorithm.
KeywordsIterative Closest Point Shared memory Approximated ICP 3D cloud registration
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