Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes

TL;DR

This paper presents a novel indoor robot heading estimation method that combines UWB range and RSS measurements with Gaussian processes and a gyroscope within an extended Kalman filter, addressing magnetic distortion issues.

Contribution

It introduces a data-driven approach using Gaussian processes to estimate robot heading from UWB signals, integrating it with a gyroscope for improved accuracy in indoor environments.

Findings

UWB RSS varies with antenna orientation.

Gaussian processes effectively model the relationship between UWB signals and heading.

The combined method improves heading estimation accuracy indoors.

Abstract

It is essential that a robot has the ability to determine its position and orientation to execute tasks autonomously. Heading estimation is especially challenging in indoor environments where magnetic distortions make magnetometer-based heading estimation difficult. Ultra-wideband (UWB) transceivers are common in indoor localization problems. This letter experimentally demonstrates how to use UWB range and received signal strength (RSS) measurements to estimate robot heading. The RSS of a UWB antenna varies with its orientation. As such, a Gaussian process (GP) is used to learn a data-driven relationship from UWB range and RSS inputs to orientation outputs. Combined with a gyroscope in an invariant extended Kalman filter, this realizes a heading estimation method that uses only UWB and gyroscope measurements.