The advancement of autonomous technology in Unmanned Aerial Vehicles (UAVs) has piloted a new era in aviation. While UAVs were initially utilized only for the military, rescue, and disaster response, they are now being utilized for domestic and civilian purposes as well. In order to deal with its expanded applications and to increase autonomy, the ability for UAVs to perform autonomous landing will be a crucial component. Autonomous landing capability is greatly dependent on computer vision, which offers several advantages such as low cost, self-sufficiency, strong anti-interference capability, and accurate localization when combined with an Inertial Navigation System (INS). Another significant benefit of this technology is its compatibility with LiDAR technology, Digital Elevation Models (DEM), and the ability to seamlessly integrate these components. The landing area for UAVs can vary, ranging from static to dynamic or complex, depending on their environment. By comprehending these characteristics and the behavior of UAVs, this paper serves as a valuable reference for autonomous landing guided by computer vision and provides promising preliminary results with static image photogrammetry.

In recent years, the research has focused firmly on Autonomous Aerial Vehicles (AAVs) owing to their vast array of potential applications, to aid those applications this study presents a technical approach for source localization and landing trajectory identification for Autonomous Aerial Vehicle (AAV) landing, leveraging computer vision and photogrammetry techniques. The proposed method aims to achieve accurate and robust localization of the landing target area and precise determination of the AAV’s landing trajectory. The source localization module utilizes a computer vision system equipped with onboard cameras and advanced image processing algorithms. The system captures images of the target area and performs feature extraction and matching to estimate the position of the landing target. Additionally, the A* algorithm serves as a pivotal tool in deriving an optimized trajectory by harnessing the relative positions of the Autonomous Aerial Vehicle (AAV) and the designated landing target.

Autonomous Aerial Vehicles (AAVs) are increasingly adopted in various applications, including surveillance, logistics, and disaster response. This study explores the integration of photogrammetry techniques to reconstruct 3D images of AAV landing areas. The motivation for this research lies in the need to enhance the safety and accuracy of AAV landings. Accurate 3D reconstructions of landing areas are essential for effective path planning to ensure the secure and precise landing of AAVs. The proposed system establishes a framework that creates precise 3D representations of the landing zone. Compared to current conventional landing procedures, this system offers real-time, high-resolution 3D insights into the landing area to enhance safety and accuracy across operations. Additionally, the letter contributes to the broader field of photogrammetry and computer vision by addressing practical challenges in AAV applications and offering solutions with far-reaching implications.

Autonomous Aerial Vehicles (AAVs) have evolved into important assets across diverse industries, owing to their remarkable versatility and adeptness in undertaking a broad spectrum of tasks. AAVs capacity for autonomous landing further enhances their utility, allowing for precise and efficient operations in various environments. This paper presents a landing area identification system for AAVs utilizing digital surface maps from onboard cameras. These maps serve as a primary reference for precise trajectory planning and obstacle avoidance during landing and photogrammetry techniques are used to refine spatial awareness, enabling dynamic adjustments. This work incorporates computer vision algorithms for the interpretation of maps and photogrammetric data, making a decision regarding altitude, approach angle, and lateral adjustments during the landing process. Validation through mathematical models demonstrates the system's effectiveness in achieving precise landings across diverse scenarios. The presented system, combining digital surface maps and photogrammetry, offers a viable solution for AAVs, improving landing accuracy in complex environments.