Robust Online Calibration for UWB-Aided Visual-Inertial Navigation with Bias Correction

TL;DR

This paper introduces a robust online calibration method for UWB anchors in visual-inertial navigation systems, explicitly accounting for localization errors and using a Kalman filter for refinement, validated through simulations and real-world tests.

Contribution

It presents a novel calibration framework that incorporates robot localization uncertainties and employs a SKF-based online refinement, improving robustness and accuracy in practical scenarios.

Findings

Enhanced calibration robustness against localization errors

Improved accuracy demonstrated in simulations and real-world experiments

Effective online refinement using a Schmidt Kalman Filter

Abstract

This paper presents a novel robust online calibration framework for Ultra-Wideband (UWB) anchors in UWB-aided Visual-Inertial Navigation Systems (VINS). Accurate anchor positioning, a process known as calibration, is crucial for integrating UWB ranging measurements into state estimation. While several prior works have demonstrated satisfactory results by using robot-aided systems to autonomously calibrate UWB systems, there are still some limitations: 1) these approaches assume accurate robot localization during the initialization step, ignoring localization errors that can compromise calibration robustness, and 2) the calibration results are highly sensitive to the initial guess of the UWB anchors' positions, reducing the practical applicability of these methods in real-world scenarios. Our approach addresses these challenges by explicitly incorporating the impact of robot localization uncertainties into the calibration process, ensuring robust initialization. To further enhance the robustness of the calibration results against initialization errors, we propose a tightly-coupled Schmidt Kalman Filter (SKF)-based online refinement method, making the system suitable for practical applications. Simulations and real-world experiments validate the improved accuracy and robustness of our approach.