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arXiv · 1511.04634

Motion Planning for Global Localization in Non-Gaussian Belief Spaces

Saurav Agarwal·Amirhossein Tamjidi·Suman Chakravorty
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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.

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Saurav Agarwal, Amirhossein Tamjidi, Suman Chakravorty. 2015-11-14. Motion Planning for Global Localization in Non-Gaussian Belief Spaces. https://arxiv.org/abs/1511.04634

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