A Bayesian Approach to Landmark Discovery and Active Perception in Mobile Robot Navigation

Abstract

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Over the past few years, localization based on landmarks has become increasingly popular. Virtually all existing approaches to landmark-based navigation, however, rely on the human designer to decide what constitutes appropriate landmarks. This paper presents an approach that enables mobile robots to select their landmarks by themselves. Landmarks are chosen based on their utility for localization. This is done by training neural network landmark detectors so as to minimize the a posteriori localization error that the robot is expected to make after querying its sensors. An empirical study illustrates that self-selected landmarks are superior to landmarks carefully selected by a human. The Bayesian approach is also applied to control the direction of the robot's camera, and empirical data demonstrates the appropriateness of this approach for active perception. The author is also affiliate...