Vision-Based Autonomous Localization of Fall Protection Anchor Points on Transmission Towers Using Multi-View Geometric Perception

This paper presents the first systematic investigation into autonomous UAV-mounted fall protection lanyard (FPL) deployment for high-voltage transmission tower inspections, addressing a critical safety gap in the power industry where falls account for 34% of occupational fatalities. We propose a novel geometry-based solution to overcome three fundamental limitations of existing approaches: the isolated processing of UAV imagery without sensor fusion, unreliable 2D-to-3D spatial cor-respondence in anchor point detection, and the high annotation costs of supervised learning methods. Our technical contribution establishes a multi-view geometric perception framework that decomposes the FPL anchoring task into ridge line identification and optimal mounting point selection. The method first develops a spacial edge distance perception algorithm specifically for power inspection drones, which computes structural depth through plane-induced homography transformations of temporally matched line features. Subsequently, a mounting position planning algorithm integrates multiview geometric constraints with practical operational requirements including ladder proximity, diagonal steel avoidance, and temporal stability. Experimental validation on real-world power infrastructure data demonstrates superior performance compared to learning-based alternatives, achieving 10.98 MAE in positioning accuracy while maintaining 80ms processing efficiency for real-time operation. The proposed approach eliminates dependency on manual climbing and expert annotations, offering both theoretical advancements in stereo-environment perception for complex structures and immediate field applicability for safer power grid maintenance. This work represents the first formal proposal and comprehensive solution for autonomous FPL deployment in transmission tower inspection scenarios.