1Robotic Embedded Systems Laboratory (Department Of Computer Science MC 0781 University Of Southern California 941 W. 37th Place Los Angeles, California, USA 90089-0781 - France)
Abstract : We describe an UAV navigation system which combines stereo visual odometry with inertial measurements from an IMU. Our approach fuses the motion estimates from both sensors in an extended Kalman filter to determine vehicle position and attitude. We present results using data from a robotic helicopter, in which the visual and inertial system produced a final position estimate within 1% of the measured GPS position, over a flight distance of more than 400 meters. Our results show that the combination of visual and inertial sensing reduced overall positioning error by nearly an order of magnitude compared to visual odometry alone.
https://hal.inria.fr/inria-00199634 Contributor : Inria Rhône-Alpes DocumentationConnect in order to contact the contributor Submitted on : Wednesday, December 19, 2007 - 11:39:26 AM Last modification on : Wednesday, August 7, 2019 - 12:14:40 PM Long-term archiving on: : Thursday, September 27, 2012 - 11:55:23 AM
Jonathan Kelly, Srikanth Saripalli, Gaurav Sukhatme. Combined Visual and Inertial Navigation for an Unmanned Aerial Vehicle. 6th International Conference on Field and Service Robotics - FSR 2007, Jul 2007, Chamonix, France. ⟨inria-00199634⟩