Céline Teulière is now assistant professor at Université Blaise Pascal (Institut Pascal)
Our approach has been validated in servoing experiments using the depth information from a Microsoft Kinect sensor. Positioning tasks are properly achieved despite the noisy measurements, even when partial occlusions or scene modifications occur.
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.
Way-point navigation has been performed on the quad-rotor UAV developed by CEA.
| 3D model-based tracking for UAV position control
| Using multiple hypothesis in 3D model-based tracking
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