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    郭慧, 潘家祯, 林大钧. 基于实数编码的多种群遗传算法的点云配准[J]. 华东理工大学学报(自然科学版), 2007, (5): 733-736.
    引用本文: 郭慧, 潘家祯, 林大钧. 基于实数编码的多种群遗传算法的点云配准[J]. 华东理工大学学报(自然科学版), 2007, (5): 733-736.
    GUO Hui, PAN Jia-zhen, LIN Da-jun. Registration of Point Cloud Data of Multi-population Genetic Algorithm Based on Real Coding[J]. Journal of East China University of Science and Technology, 2007, (5): 733-736.
    Citation: GUO Hui, PAN Jia-zhen, LIN Da-jun. Registration of Point Cloud Data of Multi-population Genetic Algorithm Based on Real Coding[J]. Journal of East China University of Science and Technology, 2007, (5): 733-736.

    基于实数编码的多种群遗传算法的点云配准

    Registration of Point Cloud Data of Multi-population Genetic Algorithm Based on Real Coding

    • 摘要: 针对不同视角下测量的点云在配准时计算量大、速度慢的缺点,提出了一种基于实数编码的多种群遗传算法的配准方法,可以克服标准遗传算法速度慢、精度差的缺点,有效地提高全局搜索能力,实验结果表明:实数编码的多种群遗传算法能够快速获得较好的配准结果,以此结果作为初始位置进行最近点迭代法配准,能迅速达到所要求的精度,获得理想的配准效果。

       

      Abstract: In order to improve the problems of large calculation and low speed in the registration of measuring data under different viewpoints,a multi-population genetic algorithm based on real coding((RMGA)) is presented.It can overcome some disadvantage of standard genetic algorithm(SGA) and has more effective in realizing the global optimization.Examples show that RMGA can achieve a better result and then the iterative closest point(ICP) algorithm can obtain a accurate registration.

       

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