Range image registration using hybrid genetic algorithm and point to plane distance based measure
基于混合遗传算法和点面距离测度的深度像配准*

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.