This paper presents an algorithm and the implementation of a motor schema to aid the visual localization subsystem of the ongoing EMILY project at Texas A and M University. The EMILY project aims to team an Unmanned Surface Vehicle (USV) with an Unmanned Aerial Vehicle (UAV) to augment the search and rescue of marine casualties during an emergency response phase. The USV is designed to serve as a flotation device once it reaches the victims. A live video feed from the UAV is provided to the casuality responders giving them a visual estimate of the USVs orientation and position to help with its navigation. One of the challenges involved with casualty response using a USV UAV team is to simultaneously control the USV and track it. In this paper, we present an implemented solution to automate the UAV camera movements to keep the USV in view at all times. The motor schema proposed, uses the USVs coordinates from the visual localization subsystem to control the UAVs camera movements and track the USV with minimal camera movements such that the USV is always in the cameras field of view.
Previous work by shows the evaluation of algorithms for the visual localization subsystem and establishes the histogram based approach to be the most effective in determining the location and orientation of the USV from the UAV's video feed @cite_9 . This paper focuses on the extension of Dufek's work by adding an autonomous system to control the camera's pan tilt to track the USV. In this section, we evaluate multiple techniques to use pan-tilt movements for visual servoing.
@cite_9
2566754498 2168628149 2063893967 569732077
This paper presents the visual localization subsystem of the ongoing EMILY project. This project aims to assist responders with establishing contact with drowning victims as quickly as possible through the use of a 1.3-meter autonomous unmanned surface vehicle (USV). Once victims are reached, the device can serve as a flotation device to support them. An unmanned aerial vehicle (UAV) provides a live video feed to responders and aids USV's navigation by visually estimating the USV's position and orientation. The movement of the UAV and the USV along with varying lighting, distance, and perspective make the task of estimating USV's position and orientation challenging. We present two implemented solutions for reliable visual localization, the first relying on color thresholding and contour detection, and the second using histograms, back-projection, and CamShift algorithm. The visual localization system was validated in four proof-of-concept field trials. Other unsuccessful methods are discussed. Highly accurate localization of a micro aerial vehicle (MAV) with respect to a scene is important for a wide range of applications, in particular surveillance and inspection. Most existing approaches to visual localization focus on indoor environments, while such tasks require outdoor navigation. Within this work, we introduce a novel algorithm for monocular visual localization for MAVs based on the concept of virtual views in 3D space. Under the assumption that significant parts of the scene do not alter their geometry and serve as natural landmarks, the accuracy of our visual approach outperforms consumer grade GPS systems. In an experimental setup we compare our approach to a state-of-the-art visual SLAM algorithm and evaluate the performance by geometric validation from an observer's view. As our method directly allows global registration, it is neither prone to drift nor bias. This makes it well suited for long-term autonomous navigation. In this paper, we control image collection for a mobile stereo camera that is actively localizing a group of mobile targets. In particular, assuming that at least one pair of stereo images of the targets is available, we propose a novel approach to control the rotation and translation of the stereo camera so that the next observation of the targets will minimize their localization uncertainty. We call this problem the Next-Best-View problem for mobile targets (mNBV). The advantage of using a stereo camera is that, using triangulation, the two simultaneous images taken by the robot during a single observation can yield range and bearing measurements of the targets, as well as their uncertainty. A Kalman filter fuses the full state history and covariance estimates, as more measurements are acquired. Our solution to the mNBV problem determines the relative transformations between camera and targets that will minimize the fused uncertainty of the targets' locations. We determine a motion plan that realizes the mNBV while respecting field of view constraints. In particular, with every new observation, we compute a new mNBV in the frame relative to the camera and subsequently realize this view in global coordinates via a gradient descent algorithm that also respects field of view constraints. Integration of mNBV with motion planning results in a hybrid system, which we illustrate in computer simulations. Abstract This paper concerns the position-based visual servo control of autonomous robotic manipulators in space. It focuses on the development of a real-time vision-based pose and motion estimation algorithm of a non-cooperative target by photogrammetry and extended Kalman filter for robotic manipulators to perform autonomous capture. Optical flow algorithm is adopted to track the target features in order to improve the image processing efficiency. Then, a close-loop position-based visual servo control strategy is devised to determine the desired pose of the end-effector at the rendezvous point based on the estimated pose and motion of the target. The corresponding desired joint angles of the robotic manipulator in the joint space are derived by the inverse kinematics of the robotic manipulator. The developed algorithm and position-based visual servo control strategy are validated experimentally by a custom built robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the proposed estimation algorithm and control scheme are feasible and effective.