基于激光点云精确定位的输电线路无人机自主巡检系统研究

Research on Autonomous Inspection System of Transmission Line UAV Based on Precise Positioning of Laser Point Cloud

以某市±500kV输电网络和500kV输电网络的整合巡线任务为个案,研究一种基于激光点云精确定位的输电线路无人机自主巡检系统。通过三角定位法、回波定位法及最小二乘法平差法将激光点云探测数据构成输电网络BIM数字化三维模型的持续维护数据,使用卷积神经元网络算法整合的二值化算法对特定坐标点的侵入物、蠕变风险提供预警数据,通过IaaS架构的API服务持续向电力值班系统提供数据支持来完善系统的BIM数字化三维模型数据和预警信息数据。从算法角度和IaaS实现角度,对该系统进行完整分析,完成激光点云精确定位的输电线路无人机自主巡检系统的研究。

Taking the integrated inspection task of±500 kV transmission network and 500 kV transmission network in a city as an example,this paper aims to study an autonomous inspection system of transmission line UAV based on laser point cloud accurate positioning.The laser point cloud detection data are formed into the continuous maintenance data of the BIM digital three-dimensional model of the transmission network through the triangle positioning method,echo positioning method and least square adjustment method,and the binary algorithm integrated with the convolution neural network algorithm is used to provide early warning data for the intrusion and creep risk of specific coordinate points.Through the API service of IaaS architecture,we continue to provide data support to the power duty system to improve the BIM digital three-dimensional model data and early warning information data of the system.From the perspective of algorithm and IaaS implementation,this paper makes a complete analysis of the system,and completes the research on the autonomous inspection system of transmission line UAV with laser point cloud accurate positioning.