基于混合遗传算法和表面间平均体积度量的深度像匹配

Range Image Registration Using a Hybrid Genetic Algorithm and Surface Mean Inter-Space Measure

提出了一种利用改进的遗传算法和表面间平均体积测度进行深度像匹配的方法.与现有基于距离的误差评判不同,该方法通过衡量深度像重叠区域内每个三角形所对应三维空间的大小来指导深度像的配准.另外,遗传算法的使用避免了困扰ICP算法的初值选取问题,退火选择、最优个体迁移以及动态的空间退化都保证文中所提出的混合遗传算法比传统的遗传算法具有更快的收敛速度,能够有效地完成深度像的精确匹配.实验结果表明该算法具有较高的配准精度,收敛速度快而且抗噪声能力强.

A novel approach is presented for precise new error metric： Surface Mean Inter-Space Measure registration of polygon meshes pair with a （SMISM）. The method is based on an improved genetic algorithm. Unlike the existing distance-based measures, the SMISM takes on the mean 3-D inter-space associated with each triangle in the overlap region to guide the range image registration. In addition, a hybrid genetic algorithm is able to register range images without need for pre-alignment, which is a key limitation always afflicting the well-known iterative closest point （ICP） method. The proposed hybrid GA, combined with the strategy of simulated annealing （SA） selection, best individual migration and dynamic parametric space degeneration, offers much faster convergence and more precise registration than the traditional GA methods. A set of experiments is designed to demonstrate that the presented method is insensitive to noises and has high precision as well as the fast convergence.