Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes
This addresses the problem of magnetic distortions in indoor heading estimation for robots, though it appears incremental as it builds on existing UWB and sensor fusion techniques.
The paper tackles robot heading estimation in indoor environments by using ultra-wideband (UWB) range and received signal strength (RSS) measurements with Gaussian processes, achieving a method that integrates with a gyroscope in an invariant extended Kalman filter.
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.