Autonomous Inspection of Power Line Insulators with UAV on an Unmapped Transmission Tower
V\'aclav Riss, V\'it Kr\'atk\'y, Robert P\v{e}ni\v{c}ka, Martin Saska

TL;DR
This paper presents an autonomous UAV inspection method for power line insulators that uses camera-LiDAR fusion and neural networks to detect and localize insulators without prior tower maps, demonstrated through simulation and real-world tests.
Contribution
It introduces a novel online inspection algorithm combining sensor fusion and deep learning for insulator detection and localization on unmapped towers.
Findings
Single-flight inspection reduces time by up to 24%.
Achieves mean localization errors of 0.16 m with low variance.
Outperforms existing methods in localization accuracy.
Abstract
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…
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Taxonomy
TopicsPower Line Inspection Robots · Robotics and Sensor-Based Localization · Remote Sensing and LiDAR Applications
