Localization and tracking under extreme and persistent sensory occlusion

Abstract

We focus on a mobile robot who must keep itself localized while closely following another robot or human. This problem has many real-world applications including that of a co-bot engaged in a follow-the-leader behavior or a robot that is participating in a convoy. If the robot is expected to eventually break away and reach its own goal, then the robot must stay self-localized. A key challenge for localization while tailing another is the extreme and persistent occlusion of the robot's sensors by the dynamic obstacle in front of it that is not modeled in its map. Current Monte Carlo localization (MCL) methods use sensor models with random noise, which are inadequate under such occlusion. We utilize a particle filter that simultaneously tracks the subject robot and the leader. We introduce novel particle weighting and adaptive sampling schemes that significantly improve the follower's localization. The result is a robust and adaptive MCL for applications involving persistent occlusion.