Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with
thermal cameras are increasingly popular for the maintenance of photovoltaic
(PV) power plants. However, automation of the inspection task is a challenging
problem as it requires precise navigation to capture images from optimal
distances and viewing angles.
This paper presents a novel localization pipeline that directly integrates PV
module detection with UAV navigation, allowing precise positioning during
inspection. Detections are used to identify the power plant structures in the
image and associate these with the power plant model. We define visually
recognizable anchor points for the initial association and use object tracking
to discern global associations. We present three distinct methods for visual
segmentation of PV modules based on traditional computer vision, deep learning,
and their fusion, and we evaluate their performance in relation to the proposed
localization pipeline.
The presented methods were verified and evaluated using custom aerial
inspection data sets, demonstrating their robustness and applicability for
real-time navigation. Additionally, we evaluate the influence of the power
plant model's precision on the localization methods.
