I received my PhD degree from Université de Rennes 1 in December 2010. My PhD thesis was a collaboration between the Laboratoire d'Intégration des Systèmes et des Technologies (L.I.S.T) of C.E.A. (The French Atomic Agency) and the Lagadic team of IRISA.
I joined the Lagadic team in 2011 to continue my research as a postdoc.
Complete list (with postscript or pdf files if available)
We propose a 3D model-based tracking suitable for indoor position control of an unmanned aerial vehicle (UAV). Given a 3D model of the edges of its environment, the UAV locates itself thanks to a robust multiple hypothesis tracker. The pose estimation is fused to inertial data to provide the translational velocity required for the control. A hierarchical control is used to achieve positioning tasks.
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Way-point navigation has been performed on the quad-rotor UAV developed by CEA.
| 3D model-based tracking for UAV position control IEEE IROS'10  |
| Using multiple hypothesis in 3D model-based tracking IEEE ICRA'10 |
We propose a vision-based algorithm to autonomously track and chase a moving target with a small-size flying UAV. The challenging constraints associated with the UAV flight led us to consider a density-based representation of the object to track. The proposed approach to estimate the target's position, orientation and scale, is built on a robust color-based tracker using a multi-part representation. This object tracker can handle large displacements, occlusions and account for some image noise due to partial loss of wireless video link, thanks to the use of a particle filter. The information obtained from the visual tracker is then used to control the position and yaw angle of the UAV in order to chase the target. A hierarchical control scheme is designed to achieve the tracking task.
The overall approach was validated on sequences with noise and occlusion.
The tracking part has been published in:
IEEE ICRA'09
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