Three Nested Kalman Filters-Based Algorithm for Real-Time Estimation of Optical Flow, UAV Motion and Obstacles Detection

Author

Kendoul, Farid ; Fantoni, Isabelle ; Dherbomez, Gérald

Author_Institution

Univ. of Technol. of Compiegne

fYear

2007

fDate

10-14 April 2007

Firstpage

4746

Lastpage

4751

Abstract

We aim at developing a vision-based autopilot for autonomous small aerial vehicle applications. This paper presents a new approach for the estimation of optical flow, aircraft motion and scene structure (range map), using monocular vision and inertial data. The proposed algorithm is based on 3 nested Kalman filters (3NKF) and results in an efficient and robust estimation process. The 3NKF-based algorithm was tested extensively in simulation using synthetic images, and in real-time experiments.

Keywords

aerospace control; aerospace robotics; collision avoidance; mobile robots; motion control; remotely operated vehicles; robot vision; 3 nested Kalman filters; UAV motion; aircraft motion; autonomous small aerial vehicle; monocular vision; obstacle detection; optical flow; range map; realtime estimation; scene structure; vision-based autopilot; Aircraft; Image motion analysis; Kalman filters; Layout; Mobile robots; Motion detection; Motion estimation; Optical filters; Remotely operated vehicles; Unmanned aerial vehicles; Small flying robots; optical flow computation; structure from motion; vision-based autopilot;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2007 IEEE International Conference on

Conference_Location

Roma

ISSN

1050-4729

Print_ISBN

1-4244-0601-3

Electronic_ISBN

1050-4729

Type

conf

DOI

10.1109/ROBOT.2007.364210

Filename

4209828