Autonomous Inspection of Power Line Insulators with UAV on an Unmapped Transmission Tower

2026-02-27

AI Summary

This study addresses the challenge of automatic insulator inspection in the absence of prior transmission tower maps by proposing a novel online inspection algorithm that fuses camera and LiDAR data. The method achieves, for the first time, fully autonomous and high-precision detection and localization of insulators within a single drone flight. It employs a convolutional neural network for insulator recognition and integrates multimodal strategies—including LiDAR point cloud projection, DBSCAN clustering, RANSAC, and PCA—to dynamically guide the drone in capturing high-resolution images in real time. Experimental results demonstrate that the proposed single-flight strategy reduces inspection time by 24% in simulation. In real-world scenarios, the system achieves horizontal and vertical localization errors of 0.16 ± 0.08 m and 0.16 ± 0.11 m, respectively, with the horizontal error variance reduced by over an order of magnitude.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 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.

Problem

• autonomous inspection
• power line insulators
• UAV
• unmapped transmission tower
• sensor fusion

Innovation

• camera-LiDAR fusion
• autonomous UAV inspection
• insulator detection and localization
• online inspection algorithm
• point cloud filtering