Abstract
We present the implementation of two visual pose estimation algorithms (object-camera and face-camera) with a control system for a low cost quadcopter for an application in a remote electronic laboratory. The objective is threefold: (i) to allow the drone to inspect instruments in the remote lab, (ii) to localize a teacher and center his face in the image for student-teacher remote communication, (iii) and to return back home and land on a platform for automatic recharge of the batteries. The object-camera localization system is composed of two complementary visual approaches: (i) a visual SLAM (Simultaneous Localization And Mapping) system, and (ii) a homography-based localization system. We extend the application scenarios of the SLAM system by allowing close range inspection of a planar instrument and autonomous landing. The face-camera localization system is based on 3D modeling of the face, and a state of the art 2D facial point detector. Experiments conducted in a remote laboratory workspace are presented. They prove the robustness of the proposed object-camera visual pose system compared to the SLAM system, the performance of the face-camera visual servoing and pose estimation system in terms of real-time, robustness and accuracy.
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Khattar, F., Dornaika, F., Larroque, B., Luthon, F. (2018). 3D Object-Camera and 3D Face-Camera Pose Estimation for Quadcopter Control: Application to Remote Labs. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_9
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