基于点对特征及分层全连接聚类的三维目标识别方法

Three-dimensional Target Recognition Method Based on Pair Point Feature and Hierarchical Complete-linkage Clustering

针对基于原始点对特征的三维目标识别算法中存在的效率低、易受干扰的问题,提出了一种分层全连接聚类算法来对三维目标进行识别。利用模型上的所有点对特征来完成全局模型的描述构建,并在局部坐标的二维空间上,利用投票方案和分层全连接聚类算法对候选位姿进行筛选,从而获得最优位姿。在UWA的数据集上的实验结果表明,与原始点对特征算法相比,所提出的分层全连接聚类算法在识别率和效率上都有一定程度的提升,并且该方法满足实用性和有效性要求。

Aiming at the problem of low efficiency and easy to be disturbed in 3 D target recognition algorithm based on original point pair features,a hierarchical compete-linkage clustering algorithm is proposed to identify 3 D targets.The global model description is constructed by using all the point pair features on the model.In the two-dimensional space of the local coordinates,the candidate pose is screened by the voting scheme and the hierarchical complete link clustering algorithm to obtain the optimal pose.Experimental results on the UWA dataset show that compared with the original point pair feature algorithm,the proposed hierarchical compete-linkage clustering algorithm has a certain degree of improvement in recognition rate and efficiency compared with the point pair feature algorithm,and the proposed method is practical and effective.