激光点云数据和图像分割相融合的目标实时定位方法

Real time target localization method based on the fusion of laser point cloud data and image segmentation

传统目标实时定位方法,在目标图像特征信息的提取上缺乏一定深度,导致定位偏差较大。因此,提出激光点云数据和图像分割相融合的目标实时定位方法。根据激光点云数据特性通过点云下采样、半径滤波器去噪等对数据进行处理,对定位目标的图像进行对称分割,将具有相似属性的特征聚类成簇,深度提取定位目标特征,采用时间索引根据提取的定位目标特征进行图像匹配,并使用基于密度的DBSCAN算法,聚类定位目标的坐标匹配数据,形成聚类的三维点集,将三维点集转换后得到定位实时坐标,对目标进行实时定位。实验结果表明,该方法在实时定位过程中偏差较小,误差在2.0%以内,具有有效性。

Traditional real-time target localization methods lack a certain depth in extracting feature information from target images,resulting in significant positioning errors.Therefore,a real-time target localization method combining laser point cloud data and image segmentation is proposed.Based on the characteristics of laser point cloud data,the data is processed through point cloud down sampling,radius filter denoising,and other methods.The image of the positioning target is symmetrically segmented,and features with similar attributes are clustered.The positioning target features are deeply extracted,and image matching is performed using time index based on the extracted positioning target features.The density based DBSCAN algorithm is used to cluster the coordinate matching data of the positioning target,Form a clustered 3D point set,convert the 3D point set to obtain real-time positioning coordinates,and perform real-time positioning of the target.The experimental results show that this method has a small deviation in the real-time positioning process,with an error of within 2.0%,and is effective.