Overcoming unknown occlusions in eye-in-hand visual search

Abstract

We propose a method for handling persistent visual occlusions that disrupt visual tracking for eye-in-hand systems. Our approach allows a robot to “look behind” an occluder and re-acquire its target. To allow efficient planning, we avoid exhaustive mapping of the 3D occluder into configuration space, and instead use informed samples to strike a balance between target search and information gain. A particle filter continuously estimates the target location when it is not visible. Meanwhile, we build a simple but effective map of the occluder's extents to compute potential occlusion-clearing motions using very few calls to efficient approximations of inverse kinematics. Our mixed-initiative cost function balances the goal of directly locating the target with the goal of gaining information through mapping the occluder. Monte-Carlo optimization with efficient data-driven proposals allows us to approximate one-step solutions efficiently. Experimental evaluation performed on a realistic simulator shows that our method can quickly obtain clear views of the target, even when occlusions are persistent and significant camera motion is required.