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

TLDR: In this paper, a Gaussian process is used to learn a data-driven relationship from UWB range and RSS inputs to orientation outputs, which is then combined with a gyroscope in an invariant extended Kalman filter.

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.