Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection

TL;DR

This paper introduces a new UAV localization method for photovoltaic plant inspection that combines PV module detection, semantic structures, and visual segmentation to enable precise, real-time navigation during aerial inspections.

Contribution

It proposes an integrated localization pipeline that uses PV module detection and semantic structures, along with three segmentation methods, validated on custom aerial datasets.

Findings

The pipeline achieves accurate UAV positioning during PV inspections.

Semantic structures improve localization robustness.

Segmentation methods enhance visual recognition of PV modules.

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. The detections are used to identify the power plant structures in the image. These are associated with the power plant model and used to infer the UAV position relative to the inspected PV installation. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. Additionally, we present three different methods for visual segmentation of PV modules and 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 precision on the localization methods.