Binocular active vision point cloud target detection based on im-proved 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 clus-tering is proposed,which uses distance threshold and angle threshold as the threshold segmentation judg-ment 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-seg-mentation,because the traditional algorithm is sensitive to the threshold,and the accuracy of target de-tection is improved,the detection effect is better in indoor scenes.
active laserstereo vision3D point cloudtarget detectionEuclidean clustering
NETL
NSTL
万方数据
维普
