Céline Teulière

Chasing a moving target from a flying UAV

This paper proposes 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 targets 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. Experiments on a quad-rotor UAV following a small moving car are provided to validate the proposed approach.

Using multiple hypothesis in model-based tracking

Classic registration methods for model-based tracking try to align the projected edges of a 3D model with the edges of the image. However, wrong matches at low level can make these methods fail. This paper presents a new approach allowing to retrieve multiple hypothesis on the camera pose from multiple low-level hypothesis. These hypothesis are integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Experiments on simulated and real video sequences show the improvement in robustness of the resulting tracker.

3D model-based tracking for UAV position control

This paper presents 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 then fused to inertial data to provide the translational velocity required for the control. A hierarchical control is used to achieve positioning tasks. Experiments on a quad-rotor aerial vehicle validate the proposed approach.

3-D Model-Based Tracking for UAV Indoor Localization

This paper proposes a novel model-based tracking approach for 3-D localization. One main difficulty of standard model-based approach lies in the presence of low-level ambiguities between different edges. In this paper, given a 3-D model of the edges of the environment, we derive a multiple hypotheses tracker which retrieves the potential poses of the camera from the observations in the image. We also show how these candidate poses can be integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Motivated by the UAV indoor localization problem where GPS signal is not available, we validate the algorithm on real image sequences from UAV flights.

Self-calibrating smooth pursuit through active efficient coding

This paper presents a model for the autonomous learning of smooth pursuit eye movements based on an efficient coding criterion for active perception. This model accounts for the joint development of visual encoding and eye control. Sparse coding models encode the incoming data at two different spatial resolutions and capture the statistics of the input in spatio-temporal basis functions. A reinforcement learner controls eye velocity so as to maximize a reward signal based on the efficiency of the encoding. We consider the embodiment of the approach in the iCub simulator and real robot. Motion perception and smooth pursuit control are not explicitly expressed as tasks for the robot to achieve but emerge as the result of the systems active attempt to efficiently encode its sensory inputs. Experiments demonstrate that the proposed approach is self-calibrating and robust to strong perturbations of the perception-action link. Efficient coding principle is used as a criterion for learning smooth pursuit eye movements.A multi-scale approach allows to perceive a large range of motions.The model is fully self-calibrating and autonomously recovers from perturbations in the perception/action link.Experiments on both simulation and iCub robot demonstrate the approach.

Direct 3D servoing using dense depth maps

This paper proposes a novel 3D servoing approach using dense depth maps to achieve robotic tasks. With respect to position-based approaches, our method does not require the estimation of the 3D pose (direct), nor the extraction and matching of 3D features (dense) and only requires dense depth maps provided by 3D sensors. Our approach has been validated in servoing experiments using the depth information from a low cost RGB-D sensor. Positioning tasks are properly achieved despite the noisy measurements, even when partial occlusions or scene modifications occur.

Autonomous Learning of Smooth Pursuit and Vergence Through Active Efficient Coding

We present a model for the autonomous and simultaneous learning of smooth pursuit and vergence eye movements based on principles of efficient coding. The model accounts for the joint development of visual encoding and eye movement control. Sparse coding models encode the incoming data and capture the statistics of the input in spatio-temporal basis functions while a reinforcement learner generates eye movements to optimise the efficiency of the encoding. We consider the embodiment of the approach in the iCub simulator and demonstrate the emergence of a self-calibrating smooth pursuit and vergence behaviour. Unlike standard computer vision approaches, it is driven by the interaction between sensory encoding and eye movements. Interestingly, our analysis shows that the emerging representations learned by this model are in line with results on velocity and disparity tuning properties of neurons in visual cortex.

A Dense and Direct Approach to Visual Servoing Using Depth Maps

This paper presents a novel 3-D servoing approach using dense depth maps to perform robotic tasks. With respect to position-based approaches, our method does not require the estimation of the 3-D pose (direct), nor the extraction and matching of 3-D features (dense), and only requires dense depth maps provided by 3-D sensors. Our approach has been validated in various servoing experiments using the depth information from a low-cost Red Green Blue-Depth (RGB-D) sensor. Positioning tasks are properly achieved despite noisy measurements, even when partial occlusions or scene modifications occur. We also show that, in cases where a reference depth map cannot be easily available, synthetic ones generated with a rendering engine still lead to satisfactory positioning performances. Application of the approach to the navigation of a mobile robot is also demonstrated.

Deep MANTA: A Coarse-to-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis from Monocular Image

In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for simultaneous vehicle detection, part localization, visibility characterization and 3D dimension estimation. Its architecture is based on a new coarse-to-fine object proposal that boosts the vehicle detection. Moreover, the Deep MANTA network is able to localize vehicle parts even if these parts are not visible. In the inference, the networks outputs are used by a real time robust pose estimation algorithm for fine orientation estimation and 3D vehicle localization. We show in experiments that our method outperforms monocular state-of-the-art approaches on vehicle detection, orientation and 3D location tasks on the very challenging KITTI benchmark.

A combination of particle filtering and deterministic approaches for multiple kernel tracking

Color-based tracking methods have proved to be efficient for their robustness qualities. The drawback of such global representation of an object is the lack of information on its spatial configuration, making difficult the tracking of more complex motions. This issue can be overcome by using several kernels weighting pixels locations.