无先验地图条件下电力管廊无人机自主巡检方法研究

针对无先验地图条件下电力管廊无人机自主巡检能力不足的问题，本文提出了一种不依赖先验地图的无人机巡检方法。首先，无人机搭载激光雷达，通过SLAM（Simultaneous Localization and Mapping）算法进行实时场景构图，并基于RRT（Rapidly-exploring Random Tree）算法对构建的全局栅格地图进行边界搜索；其次，针对传统RRT算法难以判断环境地图完整性、保障巡检全面性的问题，提出了一种基于数字图像处理的边界检测方法。通过Canny算子对实时构建的地图进行边缘检测，并对地图进行完整性评估。最后，为验证本文提出方法的有效性，在江苏无锡处某220kV电力管廊的仿真模型中进行验证，分别采用RRT算法和本文改进算法进行自主巡检测试，结果表明本文提出的算法相比RRT算法可提升21.8%的巡检覆盖率。

Inspection method employing UAV of electricity pipe gallery based on LiDAR without prior map

To tackle problem of insufficient capability of autonomous inspection employing UAV in electricity pipe gallery without prior map, this paper proposes a UAV inspection method which is independent of prior map. In the first place, the UAV is equipped with Lidar and builds the map in real time by SLAM algorithm. The UAV searches for frontiers by RRT algorithm based on global grid map. Secondly, traditional RRT algorithm is difficult to judge integrity of map and guarantee completeness of inspection. To improve coverage, a frontier detection method based on digital image processing is proposed. Canny operator is used to detect frontiers in the map which is built in real time and evaluate completeness of the map. Finally, in order to verify effectiveness of the proposed method, simulation experiments are conducted in the model of a 220kV electricity pipe gallery which is located in Wuxi, Jiangsu province. RRT algorithm and the proposed algorithm in this paper are used to carry out autonomous inspection test respectively. The results show that the proposed algorithm can improve inspection coverage by 21.8% compared with RRT algorithm.