Improving Optical Flow Sensor Using a Gimbal for Quadrotor Navigation in GPS-Denied Environment
Jonathan Flores,
Ivan Gonzalez-Hernandez,
Sergio Salazar,
Rogelio Lozano,
Christian Reyes
Affiliations
Jonathan Flores
Department of Research and Multidisciplinary Studies, Program of Aerial and Submarine Autonomous Navigation Systems, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, Mexico
Ivan Gonzalez-Hernandez
Department of Research and Multidisciplinary Studies, Program of Aerial and Submarine Autonomous Navigation Systems, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, Mexico
Sergio Salazar
Department of Research and Multidisciplinary Studies, Program of Aerial and Submarine Autonomous Navigation Systems, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, Mexico
Rogelio Lozano
Department of Research and Multidisciplinary Studies, Program of Aerial and Submarine Autonomous Navigation Systems, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, Mexico
Christian Reyes
Department of Research and Multidisciplinary Studies, Program of Aerial and Submarine Autonomous Navigation Systems, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, Mexico
This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation of the vehicle. To avoid this, we propose a novel sensor using an optical flow camera with a 6DoF IMU (Inertial Measurement Unit) mounted on a two-axis anti-shake stabilizer mobile aerial gimbal. We also propose a robust algorithm based on Sliding Mode Control for stabilizing the optical flow sensor downwards independently of the aerial vehicle attitude. This method improves the estimation of the position and velocity of the quadrotor. We present experimental results to show the performance of the proposed sensor and algorithms.