Vision-based obstacle avoidance for UAVs

This paper describes a vision-based navigation and guidance design for UAVs for a combined mission of waypoint tracking and collision avoidance with unforeseen obstacles using a single 2-D passive vision sensor. An extended Kalman filter (EKF) is applied to estimate a relative position of obstacles from vision-based measurements. The stochastic 2-test value is used to solve a correspondence problem between the measurements and the estimates that have been already obtained by then. A collision cone approach is used as a collision criteria in order to examine if there is any obstacle that is critical to the vehicle. A guidance strategy for collision avoidance is designed based on a minimum-effort guidance (MEG) method for multiple target tracking. The vision-based navigation and guidance designs suggested in this paper are integrated with realtime image processing algorithm and the entire vision-based control system are evaluated in the closed-loop 6 DoF flight simulation.