基于PSO-KNN的变电站设备三维点云识别方法研究

针对传统的三维重建方法因数据缺失而造成的精度差、效率低等问题,在三维激光扫描点云的基础上,提出了一种将粒子群优化算法和k-近邻分类算法相结合的变电站设备三维点云识别方法.通过粒子群优化算法对各子空间特征的系数权重进行优化,k-近邻分类算法完成分类.通过实验分析点云子空间的大小和丢失率对识别效果的影响,并与改进的迭代最近...

3D point cloud research on an identification method based on PSO-KNN substation equipment

There are problems of poor accuracy and low efficiency caused by the lack of data in traditional three-dimensional reconstruction methods. Thus, based on a three-dimensional laser scanning point cloud, a recognition method for substation equipment is proposed. This combines the particle swarm optimization algorithm and the k-nearest neighbor classification algorithm. The particle population optimization algorithm is used to optimize the coefficient weight of each subspace feature, and the k-nearest neighbor classification method is used to classify the equipment. The influence of the size and loss rate of the point cloud subspace on the recognition effect is analyzed through experiment. It is compared with the improved iterative closest point algorithm to verify the superiority and accuracy of the method. Experimental results show that this method can effectively improve recognition accuracy and efficiency. The recognition accuracy can reach more than 95%, and the average recognition time is 0.19 seconds, which has application value.This work is supported by the Science and Technology Project of State Grid Corporation of China (No. 5216A0182020R).