Monocular vision-based autonomous navigation system on a toy quadcopter in unknown environments

In this paper, we present an monocular vision-based autonomous navigation system for a commercial quadcoptor. The quadcoptor communicates with a ground-based laptop via wireless connection. The video stream of the front camera on the drone and the navigation data measured on-board are sent to the ground station and then processed by a vision-based SLAM system. In order to handle motion blur and frame lost in the received video, our SLAM system consists of a improved robust feature tracking scheme and a relocalisation module which achieves fast recovery from tracking failure. An Extended Kalman filter (EKF) is designed for sensor fusion. Thanks to the proposed EKF, accurate 3D positions and velocities can be estimated as well as the scaling factor of the monocular SLAM. Using a motion capture system with millimeter-level precision, we also identify the system models of the quadcoptor and design the PID controller accordingly. We demonstrate that the quadcoptor can navigate along pre-defined paths in an unknown indoor environment with our system using its front camera and onboard sensors only after some simple manual initialization procedures.