Currently I am a Ph.D. student under the supervision of Dr. Paolo Robuffo Giordano (Max Planck Institute for Biological Cybernetics) and a member of his Human-Robot Interaction group. My first doctoral advisor is Professor Andreas Schilling (Eberhard Karls Universität Tübingen, Germany; Wilhelm-Schickard-Institut für Informatik).

In the field of vertical take-off and landing vehicles, cameras became the most favored sensors for tracking and localization. However, most systems with a camera as their main sensor usually perform large portions of the computation on a powerful ground station. This clearly limits the flexibility of the system. Additionally, many presented systems lack robust backup strategies to deal with failures of the main tracking system which often relies on a map or given features.

In my work, I am addressing these limitations by relying mainly on optical flow. Having developed a robust fall-back behavior which allows the vehicle to hover in place if any more advanced system fails, I am exploiting this work towards active visual guiding with the aim of autonomous avoidance of static as well as moving obstacles.

Recently, in the field of vertical take-off and landing vehicles, cameras became the most favored sensors for tracking and localization. However, most systems with a camera as their main sensor usually perform large portions of the computation on a powerful ground station. This clearly limits the flexibility of the system. Additionally, many presented systems lack robust backup strategies to deal with failures of the main tracking system which often relies on a map or given features.

In this project, we are aiming at developing a robust system which is able to visually estimate its velocity using the optical flow obtained from a monocular camera and thus without the need to maintain a map. All computation will be done on-board the vehicle itself with only the velocity commands being transmitted to the quadrotor. Sensor fusion with an additional IMU (Inertial Measurement Unit) will be done to retrieve the scale and improve the velocity estimation.

Our work will be carried out on quadrotors manufactured from MikroKopter.de equipped with a small Intel Atom 1.6 GHz board. The final setup is shown in Figure 1. Image processing and optical flow extraction are based on the OpenCV library. To estimate the velocity, we use a customized version of the continuous four-point algorithm for planar scenes.

Figure 2 presents a comparison between the linear velocity estimated by our algorithm and the ground truth obtained from a Vicon tracking system during closed-loop control. This comparison demonstrates reliable velocity estimation with a mean error of only 0.028 m/s.

Our results show clearly that it is indeed possible to stabilize a quadrotor in closed-loop control using our algorithm on the limited hardware described. Thus, our system can be used as a robust emergency stopping behavior which does not require any knowledge of the environment (e.g. a map).

Current Position

Ph.D. Student
Topic: Vision-based localization and control of flying vehicles
Supervisor: Dr. Paolo Robuffo Giordano
Department Human Perception, Cognition and Action (Dept.Head: Heinrich H. Buelthoff), Max Planck Institute for Biological Cybernetics, Tübingen, Germany

Educational Background

References, etc.

References are available upon request.