Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles

Robust real-time obstacle detection/avoidance is a challenging problem especially for micro and small aerial vehicles due to the limited number of the on-board sensors due to the battery constraint and low payload. Usually lightweight sensors such as CMOS camera are the best choice comparing with laser or radar sensors. For real-time applications, most studies focus on using stereo cameras to reconstruct a 3D model of the obstacles or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the state of the approaching obstacles using single camera is proposed. During the flight, this method is able to detect the changes of the size area of the obstacles. First, the method detects the feature points of the obstacles, and then extracts the obstacles that has probability of getting close. In addition, by comparing the changes in the area ratios of the obstacle in the image sequence, the method can decide if it is obstacle or not. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV can take the action of avoidance.