Year: 2025
Type: conference-paper
Abstract: This paper presents the development of an autonomous air traffic control (ATC) system utilizing stereo cameras and LiDAR to enhance drone tracking accuracy, particularly in GPS-denied environments. The rapid expansion of unmanned aerial vehicles (UAVs) across various industries has created an urgent need for efficient traffic management solutions. As the number of UAVs increases, planned drone hubs are emerging as centralized points for drone operation and coordination. However, large networks of drones coming in and out of drone hubs will exceed the capabilities of current air traffic control (ATC) systems. Additionally, many environments where drones operate—such as dense urban areas, mountainous regions, or indoor facilities—pose significant challenges for GPS-based navigation. Without external positional data, drones are forced to rely on internal guidance data from inertial measurement units (IMUs) and gyroscopes, but these devices are prone to severe error propagation over time. This project addresses these limitations by integrating stereo cameras and a LiDAR unit mounted on a fixed base station, which estimates drone positions by combining external sensor data with the drone’s internal guidance systems and comparing it to GPS data. Input data comes from both the internal guidance system of the drones, including IMUs and gyroscopes, and a fixed bay station with a mounted LiDAR and stereo camera. The bay station data will then be processed using software tools like ZED and OpenCV to give a drone positional estimate relative to the position of the bay station. This generated data will then be compared against the drone’s internal sensors and GPS data to quantify error margins. The initial aim of the study is to refine the positioning algorithm and, if the accuracy reaches an acceptable threshold, enable autonomous navigation via the external tracking methods without the need for GPS input. Several challenges must be addressed to ensure the feasibility of this system. Environmental conditions such as poor lighting, rain, and other visual obstructions can degrade camera and LiDAR-based tracking performance. Similarly, interference from multiple drones in high-traffic situations can introduce additional sensor noise. To mitigate these issues, open source vision-based detection techniques such as the YOLO object detection algorithm are employed and tested against various lighting conditions. The Drone Estimation Lab (DEL) focuses on research areas that support this study, including: Depth cameras and LiDAR for real-time drone tracking. Mapping of true positions against camera/LiDAR-based external tracking. Pathfinding and obstacle avoidance as stretch goals. This study achieves experimental validation using two drones, multiple stereo cameras, and a LiDAR unit in a controlled testing environment. The data gathered is processed and visualized using MATLAB to aid in optimizing the tracking algorithms used. Given the available computational resources, the external tracking computations are not constrained by onboard processing limitations. The outcomes of this research have significant implications for future drone operations in both commercial and industrial settings. Drone hubs will benefit from precise tracking methods that allow for efficient drone traffic management even in highly congested environments. Additionally, the ability for drones to coordinate within swarms will enable more sophisticated autonomous missions, such as search and rescue operations, environmental monitoring, and package delivery logistics. Beyond individual drone performance, an autonomous drone air traffic control system will help regulate large-scale UAV movement, reducing the risk of mid-air collisions and enhancing the reliability of drone-based services in urban and remote areas. These advancements will pave the way for the widespread adoption of UAV technology in scenarios where traditional GPS-based navigation is insufficient, ultimately ensuring safer and more efficient drone integration into modern airspace.
DOI: 10.2514/6.2025-99199
Authors: Aidan Taylor, Alex Nguyen, Nicholas Christner, Saanvi Athota, Joseph Mikhail, Marco Obregon, Yixing Jiang, Mr. Adam Nokes
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Funder: University of Texas at Austin
OpenAlex ID: W4413191329