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.
, in @cite_0 shows the usage of a camera and PIR sensor fusion to track a moving object (human). However, this method has a range limitation posed by the Passive Infrared sensor. For the UAV to track the USV, an algorithm that can track the USV irrespective of the distance of the UAV from the USV, as long as it is in sight of the camera is needed. This is because, the UAV is not stationary as it keeps moving due to the effects of wind and coastal currents in the sea. Also, the PIR sensor consumes additional power and is not reliable in sunlight.
@cite_0
2240064293 2139241879 1960629836 2512653618
One of fundamental issues for security robots is to detect and track people in the surroundings. The main problems of this task are real-time constraints, a changing background, varying illumination conditions and a non-rigid shape of the person to be tracked. In this paper, we propose a solution for tracking with a pan-tilt camera and a passive infrared range (PIR) sensor to detect the moving object based on consecutive frame difference. The proposed method is excellent in real-time performance because it requires only a little memory and computation. Experiment results show that this method can detect the moving object such as human efficiently and accurately in non-stationary and complex indoor environment. The motivation of this research is to show that visual based object tracking and following is reliable using a cheap GPS-denied multirotor platform such as the AR Drone 2.0. Our architecture allows the user to specify an object in the image that the robot has to follow from an approximate constant distance. At the current stage of our development, in the event of image tracking loss the system starts to hover and waits for the image tracking recovery or second detection, which requires the usage of odometry measurements for self stabilization. During the following task, our software utilizes the forward-facing camera images and part of the IMU data to calculate the references for the four on-board low-level control loops. To obtain a stronger wind disturbance rejection and an improved navigation performance, a yaw heading reference based on the IMU data is internally kept and updated by our control algorithm. We validate the architecture using an AR Drone 2.0 and the OpenTLD tracker in outdoor suburban areas. The experimental tests have shown robustness against wind perturbations, target occlusion and illumination changes, and the system's capability to track a great variety of objects present on suburban areas, for instance: walking or running people, windows, AC machines, static and moving cars and plants. For the control of unmanned aerial vehicles UAVs in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit IMU. First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the unknown metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter EKF formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions. This paper reports an object tracking algorithm for a moving platform using the dynamic and active-pixel vision sensor (DAVIS). It takes advantage of both the active pixel sensor (APS) frame and dynamic vision sensor (DVS) event outputs from the DAVIS. The tracking is performed in a three step-manner: regions of interest (ROIs) are generated by a cluster-based tracking using the DVS output, likely target locations are detected by using a convolutional neural network (CNN) on the APS output to classify the ROIs as foreground and background, and finally a particle filter infers the target location from the ROIs. Doing convolution only in the ROIs boosts the speed by a factor of 70 compared with full-frame convolutions for the 240×180 frame input from the DAVIS. The tracking accuracy on a predator and prey robot database reaches 90 with a cost of less than 20ms frame in Matlab on a normal PC without using a GPU.