This paper introduces an online inspection algorithm that enables an autonomous UAV to fly around a transmission tower and obtain detailed inspection images without a prior map of the tower. Our algorithm relies on camera-LiDAR sensor fusion for online detection and localization of insulators. In particular, the algorithm is based on insulator detection using a convolutional neural network, projection of LiDAR points onto the image, and filtering them using the bounding boxes. The detection pipeline is coupled with several proposed insulator localization methods based on DBSCAN, RANSAC, and PCA algorithms. The performance of the proposed online inspection algorithm and camera-LiDAR sensor fusion pipeline is demonstrated through simulation and real-world flights. In simulation, we showed that our single-flight inspection strategy can save up to 24 % of total inspection time, compared to the two-flight strategy of scanning the tower and afterwards visiting the inspection waypoints in the optimal way. In a real-world experiment, the best performing proposed method achieves a mean horizontal and vertical localization error for the insulator of 0.16 +- 0.08 m and 0.16 +- 0.11 m, respectively. Compared to the most relevant approach, the proposed method achieves more than an order of magnitude lower variance in horizontal insulator localization error.

翻译：本文提出一种在线巡检算法，使自主无人机能够在无先验塔架地图的情况下环绕输电塔飞行并获取详细巡检图像。该算法通过相机-激光雷达传感器融合实现在线绝缘子检测与定位。具体而言，算法基于卷积神经网络进行绝缘子检测，将激光雷达点云投影至图像平面，并利用边界框进行点云过滤。检测流程与基于DBSCAN、RANSAC和PCA算法提出的多种绝缘子定位方法相结合。通过仿真与真实飞行实验验证了所提在线巡检算法及相机-激光雷达融合流程的性能。仿真结果表明：相较于先扫描塔架再以最优路径访问巡检航点的双次飞行策略，我们提出的单次飞行巡检策略可节省高达24%的总巡检时间。在真实实验中，性能最优的提出方法对绝缘子的水平与垂直定位误差均值分别为0.16±0.08米与0.16±0.11米。与最相关方法相比，所提方法在绝缘子水平定位误差方差上降低了一个数量级以上。