Detection of fast incoming objects with a moving camera

Using a monocular camera for early collision detection in cluttered scenes to elude fast incoming objects is a desirable but challenging functionality for mobile robots, such as small drones. We present a novel moving object detection and avoidance algorithm for an uncalibrated camera that uses only the optical flow to predict collisions. First, we estimate the optical flow and compensate the global camera motion. Then we detect incoming objects while removing the noise caused by dynamic textures, nearby terrain and lens distortion by means of an adaptively learnt background-motion model. Next, we estimate the time to contact, namely the expected time for an incoming object to cross the infinite plane defined by the extension of the image plane. Finally, we combine the time to contact and the compensated motion in a Bayesian framework to identify an object-free region the robot can move towards to avoid the collision. We demonstrate and evaluate the proposed algorithm using footage of flying robots that observe fast incoming objects such as birds, balls and other drones.