一种快速变电站设备三维点云识别方法

A Fast 3D Point Cloud Recognition Method for Substation Equipment

针对变电站设备点云识别的问题,提出一种新的识别方法。首先,建立设备点云的局部坐标系,利用设备点云在空间中的对称性和分布密度,确定局部坐标系的x、y轴,该坐标系具有平移和旋转不变性,并且对噪声鲁棒。其次,结合变电站设备点云的形状和设备视图的差异,定义一种新的特征描述子,用于设备点云的描述和识别,并建立一个包含避雷器、断路器、隔离开关等54种电气设备的模板库,库中包含了每个模板设备的型号、编号以及模板设备的特征描述子信息。通过计算待识别的设备点云的特征描述子,将其和模板库中的特征描述子进行匹配,找到匹配误差最小的模板,完成对设备点云的识别。最后,在90个待识别设备点云上对本文算法和另外两种变电站点云识别算法进行验证,结果表明:本文算法可以达到90%的识别准确率,识别一个设备的平均时间为3.2 s,可以较好地平衡识别准确率和识别效率,并且当待识别点云中存在噪声和遮挡时,本文算法的识别准确率略高于另外两种算法,当待识别点云密度不均匀时,本文算法仍然能保持70%以上的识别准确率。

Aiming at the problem of substation equipment point cloud recognition,this study proposed a new recognition method.Firstly,a local coordinate system of equipment point cloud was established.The symmetry and distribution density of equipment point cloud were used to determine the x and y axes of the local coordinate system.The coordinate system was invariant to translate and rotate,and was robust to noise.Then,a new feature descriptor was defined based on the difference between the shape and view of substation equipment point cloud,which was used to describe and recognize the point cloud of equipment.Also,a template library containing 54 kinds of electrical equipment such as lightning arrester,circuit breaker,and disconnecting switch was established,which contained the information of type,number and feature descriptor of each template device.The feature descriptor of the equipment point cloud to be identified was calculated and was used for matching the most similar model in the template library to realize the recognition of equipment point cloud.Finally,the method proposed in this study and another two substation point cloud recognition algorithms were tested on 90 equipment point clouds to be identified.The results showed that our method could achieve 90% recognition accuracy,and the average time to identify a device was 3.2 s,which could balance the recognition accuracy and recognition efficiency.And our method slightly outperformed the other two methods when equipment point cloud with noise and occlusion.Moreover,when the density of the point cloud to be identified was not uniform,our method could still maintain the recognition accuracy of more than 70%.