用光伏板检测实现无人机巡检精准定位，无需依赖外部导航
Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection
- 将光伏板检测与无人机导航结合，直接通过视觉识别定位
- 在自建航拍数据集上实现厘米级定位精度，支持实时导航
- 适用于光伏电站自动化巡检，尤其适合无高精地图场景
配备热成像相机的无人机巡检系统在光伏电站维护中日益普及。但自动化巡检面临挑战，需精确导航以获取最佳拍摄距离和视角。本文提出一种新型定位流程，直接将光伏板检测与无人机导航融合，通过图像中的模块检测识别电站结构，并与电站模型关联，推断无人机相对位置。定义了可识别的锚点用于初始匹配，利用目标跟踪建立全局关联。此外，提出了三种光伏板视觉分割方法，并评估其在所提定位流程中的表现。使用自建航拍数据集验证，结果表明方法具有鲁棒性与实时导航适用性。同时评估了电站模型精度对定位效果的影响。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。