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Visual localization and servoing for drone use in indoor remote laboratory environment

Abstract : In this paper, we present a localization system for the use of drone in a remote lab. The objective is to allow a drone to inspect remote electronic instruments autonomously, as well as to return to its base and land on a platform for the recharge of its batteries. In addition, the drone should be able to detect the presence of a teacher in the lab, and to center the human face in the image in order to enable remote student-teacher communication. To achieve the first objective, the localization approach is composed of a monocular SLAM (Simultaneous Localization and Mapping) algorithm PTAM (Parallel Tracking and Mapping) and an estimation based on the homography transform. For the face-drone servoing, the approach is based on the 3D Candide model. Both approaches work in real-time. Quantitative and qualitative experiments are presented that show the robustness of both methods.
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https://hal-cnrs.archives-ouvertes.fr/hal-03126470
Contributor : Franck Luthon <>
Submitted on : Sunday, January 31, 2021 - 3:56:01 PM
Last modification on : Friday, February 5, 2021 - 3:31:41 AM
Long-term archiving on: : Saturday, May 1, 2021 - 6:09:29 PM

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Fawzi Khattar, Franck Luthon, Benoit Larroque, Fadi Dornaika. Visual localization and servoing for drone use in indoor remote laboratory environment. Machine Vision and Applications, Springer Verlag, 2021, 32 (1), pp.32. ⟨10.1007/s00138-020-01161-7⟩. ⟨hal-03126470⟩

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