The use of unmanned aerial vehicles (UAVs) has increased rapidly, leading to an effort to accurately and efficiently track UAVs. Many existing approaches utilize YOLO, a state-of-the-art object detection model, in conjunction with object tracking algorithms to detect and follow UAVs in real-time. However, these systems typically focus on a single method, without considering alternative tracking methods. In this paper, we present an experimental comparison of multiple object tracking algorithms integrated with YOLOv8, offering a comprehensive evaluation of their performance in UAV tracking scenarios. First, the model size was optimized to determine the best balance between speed and accuracy. Then, various tracking methods are tested to determine the most effective combination. The YOLOv8 model combined with a Kernelized Correlation Filter outperformed various other trackers in varying environmental scenarios, with a combined success rate and a tracking accuracy of 0.8041. This approach was further implemented in real-time on a Jetson Orion Nano GPU, utilizing a pan-tilt gimbal and an Intel RealSense D435i camera. Running at 20 FPS, the system demonstrated robustness and stability during motion and various environmental scenarios, highlighting its potential for integration into applications such as ground-based UAV surveillance.

Obstacles on railroads significantly increase the risk of traveling with a lot of train accidents caused by undetected obstacles. The obstacles disturb both the shipments of goods and the transportation of people leading to delays and damage which then result in substantial financial losses. Following natural disasters, manually locating and removing obstacles is not only time-consuming but also hazardous for the personnel involved. To address these challenges, this paper proposes an object detection system that can be implemented on an aerial drone to detect obstacles on the railway. This approach aims to enhance railway safety, reduce costs, and ensure the timely delivery of essential goods such as food and medical supplies during emergencies.

Visual monitoring operations underwater require both observing the objects of interest in close-proximity, and tracking the few feature-rich areas necessary for state estimation. This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs) to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple objectives along the path within a specified proximity. The method is sufficiently fast to be executed in real-time and is suitable for single or multiple camera configurations. Experimental results show the significant improvement on tracking multiple automatically-extracted points of interest, with low computational overhead and fast re-planning times.Accompanying short video: https://youtu.be/JKO bbrIZyU

A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct and indirect methods that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.

A semi-autonomous mobile self-leveling landing platform designed to launch, recover and re-launch VTOL UAVs without the need for human intervention is described. The landing platform is rugged, lightweight and inexpensive, making it ideal for civilian applications that require a base station from which a rotorcraft UAV can be launched and/or recovered on terrain that is normally unsuitable for UAV take-off and landing. This landing platform is capable of autonomously self-leveling on rough terrain and inclines up to 25°, and can operate in isolated remote locations for extended periods of time using large onboard lithium batteries and wireless communication. The unique design aspects of this landing platform are that it is mobile, self-leveling, and man-portable. A fully-operational prototype has been designed, constructed and evaluated. Design details and experimental results are presented to demonstrate the landing platform's functionality, and that all primary design requirements have been met.