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

2025-08-14

AI Summary

In Ultra-Wideband (UWB)-aided visual-inertial navigation, online anchor calibration suffers from high sensitivity to initial pose estimation errors and poor robustness. To address this, we propose a tightly coupled, robust online calibration framework. Our approach explicitly models robot pose uncertainty as state covariance and employs a Schmidt Kalman Filter to jointly optimize visual, inertial, and UWB measurements—thereby eliminating the strong dependence on anchor initial guesses and initialization accuracy inherent in conventional methods. The framework supports real-time bias correction and fine-tuning without requiring prior high-precision maps or manual calibration. Extensive simulations and real-world experiments demonstrate that our method reduces anchor localization error by over 42% compared to state-of-the-art approaches, significantly improving autonomous calibration accuracy and convergence stability in complex environments.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Addresses UWB anchor calibration robustness in VINS
• Mitigates sensitivity to initial anchor position guesses
• Incorporates robot localization uncertainties for accuracy

Innovation

• Robust online UWB anchor calibration framework
• Incorporates robot localization uncertainties explicitly
• Schmidt Kalman Filter-based online refinement method