基于点云属性信息的平面标靶特征提取

目的 色彩和回光强度作为点云的属性信息可以有效地应用到点云数据的特征提取中.本文根据平面标靶的几何以及光谱特性,提出了一种平面标靶的快速提取算法用于精确确定靶心坐标.方法 首先根据点云的色彩信息,采用光谱欧式距离函数进行点云的分类;对分类后的点云进行色彩重采样以提高同类点云色彩的一致性,同时根据平面标靶点云的光谱特性,根据其RGB值进行目标区域的提取,通过对圆形区域的面积测定以确定目标提取的正确性.最终根据点的回光强度值,确定圆形目标区域的中心坐标位置.结果 通过两组实验分析,第1组对固定距离处的标靶靶心提取精度分析,本文自动提取靶心的点位中误差为3.31 mm,而手工提取靶心的点位中误差为11.78 mm,证明本文方法的提取精度明显高于手工提取靶心的精度;第2组对分散排列的标靶靶心提取精度分析,通过分析证明本文方法在5 m处的靶心提取精度可以优于2 mm;10 m处的提取精度可以优于4 mm;15 m 处的点位提取精度可以优于5 mm.结论证实了本文方法的可行性和精确性.

Feature extraction of flat target based on point cloud attribute information

Objective Color and intensity which are attributes of a point cloud could be effectively used in the feature extraction of a point cloud. Base on the geometrical and spectral characteristics of the flat target, this paper presents a fast flat target extraction algorithm to determine the coordinate of the target center. Method First, spectral Euclidean distance function is adopted to classify the point cloud based on color information and then a method of color resampling is used to improve the color consistency in the same category. The extraction of the target area is implemented by the values of RGB in terms of the spectral property of the flat target. In addition, the target's validity is confirmed by measuring the area of the extracted circle field. Finally, according to the intensity values of the points, the center of the target area is determined. Result The feasibility and accuracy of the proposed method are demonstrated by two groups of experiments. The first experiment was conducted by fixing the distance to analyze the extraction accuracy of the center of the target. Comparing with the mean square error of 11.78 mm of the manual extraction method, the mean square error of automatic extraction of the proposed method can reach to 3.31 mm, which is obviously more accurate than the method of manual extraction; and the second experiment involveal an analysis by arranging the targets in different places to get the center coordinates. Through the analysis the following results can be obtained that the extraction accuracy could be better than 2 mm at the distance of 5 m, and be better than 4 mm at the distance of 10 as well as 5 mm at the distance of 15 m, respectively. The feasibility and accuracy of the proposed method are also demonstrated by the experiments conducted in this paper as well.