2023/04/03 by Iacopo Catalano, Catalano, Iacopo, Jorge Peña Queralta +3
Computer Science · Engineering · #FOS: Computer and information sciences #Robotics (cs.RO) #Robotics and Sensor-Based Localization #UAV Applications and Optimization #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2304.00780
openalex publication_date 2023/04/03 · openalex created_date 2023/04/06 · openalex updated_date 2026/07/28
Drones have become essential tools in a wide range of industries, including agriculture, surveying, and transportation. However, tracking unmanned aerial vehicles (UAVs) in challenging environments, such cluttered or GNSS-denied environments, remains a critical issue. Additionally, UAVs are being deployed as part of multi-robot systems, where tracking their position can be essential for relative state estimation. In this paper, we evaluate the performance of a multi-scan integration method for tracking UAVs in GNSS-denied environments using a solid-state LiDAR and a Kalman Filter (KF). We evaluate the algorithm's ability to track a UAV in a large open area at various distances and speeds. Our quantitative analysis shows that while "tracking by detection" using a constant velocity model is the only method that consistently tracks the target, integrating multiple scan frequencies using a KF achieves lower position errors and represents a viable option for tracking UAVs in similar scenarios.