Motion Planning for Global Localization in Non-Gaussian Belief Spaces

TL;DR

This paper introduces a receding horizon motion planning method for global localization that effectively disambiguates multimodal beliefs in non-Gaussian spaces, demonstrated through real robot experiments in complex environments.

Contribution

It proposes a novel receding horizon approach for planning actions that resolve multimodal uncertainties in non-Gaussian belief spaces for robot localization.

Findings

Successfully localized a robot with no initial pose information.

Demonstrated effective disambiguation of multimodal beliefs in real-world maze environments.

Achieved finite-time convergence to the correct robot pose.

Abstract

This paper presents a method for motion planning under uncertainty to deal with situations where ambiguous data associations result in a multimodal hypothesis on the robot state. In the global localization problem, sometimes referred to as the "lost or kidnapped robot problem", given little to no a priori pose information, the localization algorithm should recover the correct pose of a mobile robot with respect to a global reference frame. We present a Receding Horizon approach, to plan actions that sequentially disambiguate a multimodal belief to achieve tight localization on the correct pose in finite time, i.e., converge to a unimodal belief. Experimental results are presented using a physical ground robot operating in an artificial maze-like environment. We demonstrate two runs wherein the robot is given no a priori information about its initial pose and the planner is tasked to localize the robot.