Modeling Robot Orientation Using WiFi Directional Antennas and Von Mises Distributions

Abstract

Global localization allows a robot to determine its own pose (position and orientation) within a known map of its environment without having any prior information about its initial pose, and it is essential for robust localization and recovery after localization failure. Previous works have realized global position estimation indoors using WiFi signal strength, even in visually and geometrically similar environments - with the robot's orientation being estimated by its change in position over time. By using directional antennas and modeling the angle-of-arrival of WiFi signals, this work proposes a method to estimate global orientation directly. This method can be used for single-shot global pose estimation or for initializing a Bayesian Estimator.