基于改进欧式聚类算法的双目主动视觉点云目标检测研究

双目视觉立体匹配时,在同色调表面因为缺乏纹理信息,不仅计算量大且匹配度低, 而且生成的 场景中的点云又具有非结构化、近密远疏的性质, 因此,提高双目视觉匹配的精度与速度, 以及准确分割点 云目标, 一直是点云获取及目标检测中的难点问题。 针对以上问题, 本文首先提出了一种融合主动激光 的 3D 点云目标采集方法, 快速准确地获得原始点云数据； 其次提出了一种基于欧式聚类的改进算法, 使用距离阈值和角度阈值作为阈值分割判断条件进行分段聚类, 得到边界明确的 3D 点云目标检测框。 实验结果表明:所设计的 3D 点云成像系统能够有效获取前方物体的 3D 点云信息,且具有比激光雷达成 本低、易实现、信息丰富等优势；改进后的欧式聚类算法能有效改善传统算法对阈值较为敏感导致的物 体易出现欠分割或过分割的问题, 提高了目标检测的准确率, 在室内场景下具有良好的检测效果。

Binocular active vision point cloud target detection based on improved euclidean clustering algorithm

In stereo matching of binocular vision, due to the lack of texture information on the same-tone surface,not only has a large amount of computation but also has low matching degree,and the point cloud in the generated scene has the nature of unstructured,the near is dense and the far is sparse.Therefore,improving the matching accuracy and speed of binocular vision,and accurate segmentation of the target has been a difficult problem in point cloud acquisition and target detection.To solve the above problems,firstly,a 3D point cloud target acquisition method combining active laser is proposed to obtain the original point cloud data quickly and accurately.And an improved algorithm based on Euclidean clustering is proposed,which uses distance threshold and angle threshold as the threshold segmentation judgment conditions to perform segmented clustering,the 3D point cloud target detection box with clear boundary is obtained. The experimental results show that the designed 3D point cloud imaging system can effectively obtain the 3D point cloud information of the target in front, and has the advantages of lower cost, easier implementation and more information than lidar.The improved Euclidean clustering algorithm can effectively solve the problem that the object is prone to under-segmentation or over-segmentation,because the traditional algorithm is sensitive to the threshold,and the accuracy of target detection is improved,the detection effect is better in indoor scenes.