| 基于天牛须改进粒子群算法的点云配准方法 |
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| 引用本文： | 陈斯祺,张海洋,赵长明,张子龙,王文鑫,张明.基于天牛须改进粒子群算法的点云配准方法[J].激光技术,2020,44(6):678-683. |
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| 作者姓名： | 陈斯祺 张海洋 赵长明 张子龙 王文鑫 张明 |
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| 作者单位： | 北京理工大学 光电学院 光电成像技术与系统教育部重点实验室，北京 100081 |
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| 基金项目： | 国家重点研发计划资助项目 |
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| 摘 要： | 为了提高激光点云配准精度与配准速度，采用了基于天牛须算法改进的粒子群算法，以点云分布熵为寻优目标, 寻找最优空间变换矩阵的点云粗配准，为点云精配准提供良好的初始条件。结果表明，点云分布熵较传统的均值平方差评价方式有更快的计算速度，基于天牛须算法改进的粒子群算法具有全局搜索能力强、计算速度快等特点，与传统点云粗配准方法相比，该方法配准速度提升了近25%;在点云数据量大的条件下，表现出较快的配准速度。这一方法对如何提高激光点云配准速度具有参考意义。
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| 关 键 词： | 激光技术 激光雷达点云配准 点云分布熵 粒子群算法 天牛须算法 |
| 收稿时间： | 2019-11-25 |
Point cloud registration method based on particle swarm optimization algorithm improved by beetle antennae algorithm |
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| Abstract: | In order to improve the accuracy and speed of LiDAR point cloud registration, a point cloud coarse entropy that used particle swarm algorithm based on the improved beetle algorithm was adopted. The method useed the point cloud distribution entropy as the optimization target to find the optimal spatial transformation matrix, and provided a good initial condition for the precise registration of the point cloud. Through theoretical analysis and simulation verification, it was proved that the point cloud entropy is an evaluation method which has faster calculation speed than the traditional mean squared difference, and the particle swarm optimization algorithm improved by beetle antennae algorithm has the characteristics of strong global search ability and fast calculation speed. Compared with the traditional point cloud coarse registration method, the registration method has improved the registration speed by nearly 25%. Under the condition of large amount of point cloud data, it still shows a faster registration speed. This method has a reference significance for how to improve the registration speed of laser point cloud. |
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