Resolving Occlusion in Active Visual Target Search of High-Dimensional Robotic Systems

Abstract

We propose an algorithm for handling visual occlusions that disrupt visual tracking of high-dimensional eye-in-hand systems. Our algorithm allows a robot to look behind an occluder during active visual target search and reacquire its target in an online manner. A particle filter continuously estimates the target location and an enhanced observation model updates the target belief state. Meanwhile, we build a simple but efficient map of the occluder boundaries to compute potential occlusion-clearing motions. Our mixed-initiative cost function balances the goal of gaining more information about the target and occluder boundary while minimizing the sensor action cost. A data-driven planner uses informed samples to strike a balance between target search and information gain to avoid exhaustive mapping of the three-dimensional occluder into Configuration space. We demonstrate the capabilities of our algorithm in simulation and a real-world experiment. We also show that our proposed solvers outperform a common approach in the literature. Our results indicate that our algorithm can quickly obtain clear views of the target when occlusion is persistent and significant camera motion is required.