Perception process of the surroundings of UAV systems in urban environments - Chavarría-Zamora, Luis Alberto (2026)
Unmanned Aerial Vehicle (UAV) systems have gained importance in various applications due to their ability to operate in hazardous or hard-to-reach environments. However, safe and efficient navigation in complex environments, such as urban areas or indoor spaces, presents significant challenges in terms of environmental perception. Accurate perception of the surrounding environment is crucial for a UAV to build a 3D map, detect and avoid obstacles, locate points of interest, and plan safe flight paths. To address these challenges, UAV systems integrate a variety of sensors, such as RGB and depth cameras, radars, LiDARs, and inertial sensors. These sensors enable the capture of visual, geometric, and motion information from the environment. Additionally, advanced techniques in signal processing, computer vision, machine learning, and sensor fusion are employed to interpret sensor data and construct an accurate 3D representation of the environment. In the presented work, a method for indoor path exploration was proposed by detecting doors and hallways using YOLO in combination with monocular background estimation. The paths were validated, and with this information, maps were generated to explore a given area. An accuracy rate of 86.9% was achieved, although some false positives occurred where the model did not correctly detect depth. The generated map keeps a history of the agent’s explored territory, serving as a potential option for integration with swarm algorithms.