摘要
为了解决在散杂货港口中通过4D毫米波雷达扫描桥式卸船机抓斗得到的点云稀疏不完整,难以描述抓斗轨迹的问题,提出一种基于果蝇优化算法的点云配准改进方法,以补充4D毫米波雷达获取的点云,进而更准确描述卸船机抓斗的轨迹。首先,利用激光雷达扫描获取相对完整的点云,将其作为模板点云,提取其内部形状特征点;然后,分别获取模板点云的特征点与待配准的稀疏目标点云的快速点特征直方图(FPFH),根据计算出的FPFH描述子进行特征匹配;最后,通过改进的果蝇优化算法进一步获取模板点云和目标点云间的位置关系,并利用迭代最近点算法实现点云精配准。利用斯坦福点云库中的部分模型和实测的桥式卸船机点云数据分别进行验证,实验结果表明:与5种近年来常用的配准算法相比,改进后的配准算法的均方根误差降低了超过7.1%,平均绝对误差降低了超过8.7%。所提方法在配准稀疏点云时误差较小,精度更高,在连续配准时,所得抓斗轨迹平滑稳定。
Point clouds obtained by scanning the bridge unloader grab bucket with 4D millimeter-wave radar in bulk cargo ports are sparse and incomplete,making it difficult to describe the grab bucket trajectory.To address this problem,an improved point cloud registration method based on the fruit fly optimization algorithm is proposed.This supplements the point cloud obtained by 4D millimeter-wave radar by a more accurate description of the grab bucket trajectory of the unloader.First,lidar scanning is used to obtain a relatively complete point cloud,which is used as a template to extract the intrinsic shape signatures of the cloud.Subsequently,the feature points of the template point cloud and fast point feature histogram(FPFH)of the sparse target point cloud are separately registered,and feature matching is performed based on the calculated FPFH descriptors.Finally,the improved fruit fly optimization algorithm is used to obtain the positional relationship between the template and target point clouds,followed by the iterative closest point algorithm to achieve precise point cloud registration.Partial models from the Stanford point cloud database and measured point cloud data from bridge unloaders are used to compare five commonly used registration algorithms.The experimental results show that the improved registration algorithm reduces the root mean square error by more than 7.1%and mean absolute error by more than 8.7%.The proposed method has smaller errors and higher accuracy in registering sparse point clouds,and the resulting grab trajectory is smooth and stable in continuous alignment.
作者
白中选
沈阅
孔德明
Bai Zhongxuan;Shen Yue;Kong Deming(School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China;Hebei YSUSOFT Co.,Ltd.,Qinhuangdao 066000,Hebei,China)
出处
《激光与光电子学进展》
北大核心
2025年第14期357-367,共11页
Laser & Optoelectronics Progress
基金
航空科学基金(20200016099002)。
关键词
遥感
毫米波雷达
稀疏点云
点云配准
果蝇优化算法
运动目标跟踪
remote sensing
millimeter-wave radar
sparse point cloud
point cloud registration
fruit fly optimization algorithm
moving target tracking
