Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection

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.

Wearable camera-based human absolute localization in large warehouses

In a robotised warehouse, as in any place where robots move autonomously, a major issue is the localization or detection of human operators during their intervention in the work area of the robots. This paper introduces a wearable human localization system for large warehouses, which utilize preinstalled infrastructure used for localization of automated guided vehicles (AGVs). A monocular down-looking camera is detecting ground nodes, identifying them and computing the absolute position of the human to allow safe cooperation and coexistence of humans and AGVs in the same workspace. A virtual safety area around the human operator is set up and any AGV in this area is immediately stopped. In order to avoid triggering an emergency stop because of the short distance between robots and human operators, the trajectories of the robots have to be modified so that they do not interfere with the human. The purpose of this paper is to demonstrate an absolute visual localization method working in the challenging environment of an automated warehouse with low intensity of light, massively changing environment and using solely monocular camera placed on the human body.