Visual Quadrotor Swarm for IMAV 2013 Indoor Competition

This paper presents a whole swarm system for low-cost visual navigation, developed to participate in the 2013 IMAV Indoor Flight Competition. The quadrotor platform employed is the Parrot ARDrone 2.0. ArUco Codes [2] are used to sense and map the obstacles and to improve the pose estimation based on the IMU data and optical flow by means of an Extended Kalman Filter localization and mapping method. A free-collision trajectory for each drone is generated by using a combination of well-known trajectory planning algorithms: probabilistic road maps, the potential field map algorithm and the A-Star algorithm. The control loop of each drone of the swarm is closed by a robust mid-level controller. A very modular design for integration within the Robot Operating System (ROS) [13] is proposed.