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Range image registration using hybrid genetic algorithm and point to plane distance based measure

Keywords: genetic algorithm,point to plane distance,error metric,range image registration

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This paper presented a novel approach for precise registration of range images pair with an improved genetic algorithm(GA) and a new error metric based on the point to plane distance. Different to the existed ICP methods, this approach formulated the surface registration as a high dimensional optimization problem. Then combined the strategy of simulated annealing(SA) selection, hill climbing and dynamic parametric space degeneration into a GA to offer much faster convergence and more precise registration. At the same time, employed a new measure based on the point to plane distance as fitness function to evaluate the alignment error, which made the approach more robust. A number of experiments demonstrate that the presented method is insensitive to noises as well as the initial pose estimation and has high precision and fast convergence.


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