Real-Time Drone Position and Pose Detection Using RGB-D Camera
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/5th September 2016, Isabella Gonzalez, Mohit Hingorani, Oytun Ulutan/ Object detection and tracking is one of the most fundamental problems in computer vision. In recent years drones have become widely available and are being used for purposes ranging from military, air delivery as well as for art making. This project aims at developing a novel way of tracking drones and their orientation with respect to the camera. It can essentially replace more expensive tracking systems such as opti-tracking. The algorithm consists of a depth based contour finder to detect clean contours.
As the Kinect data is noisy, the depth pixels are aggregated into strips for a more robust depth estimation. After this aggregation, we calculate the difference between adjacent strips to find where the sign changes. This change records the edge of the drone. A similar mechanism was implemented in the color domain for more robust detection. Lastly using the drone’s depth value and its pixel position, we calculate its position in real world space.
This paper describes the implementation of a robust drone position and pose detection algorithm. This was created to serve as a cheaper and more flexible alternative to the opti-track system. The application was built using Open-Frameworks to target the creative coding community allowing interactive performances using drones as a medium. It employs various computer vision techniques to process the Kinect’s depth and RGB data to detect the drones in space. For this we used open frameworks addons for the kinect and computer vision namely ofxCV and ofxKinect. OpenCV allowed us to quickly manipulate images and process the depth information allowing real time tracking. Concurrently a mesh is created from the drone movements which is scaled down for 3D visualization. The visualization also displays the pose of the drone.
The full research paper you can download from here: Real-Time Drone Position and Pose Detection Using RGB-D Camera
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