CVIRO: A Consistent and Tightly-Coupled Visual-Inertial-Ranging Odometry on Lie Groups

2025-08-14

AI Summary

In Ultra-Wideband (UWB)-aided visual-inertial odometry (VIO), state estimation inconsistency arises from observability mismatch and erroneous prior assumptions about UWB anchor positions. To address this, we propose a tightly coupled invariant filtering framework on Lie groups. Our method explicitly incorporates UWB anchor positions into the joint state vector and models their calibration uncertainty. Leveraging invariant error design on Lie groups, it rigorously preserves the system’s observable subspace structure, thereby guaranteeing theoretical estimation unbiasedness. The framework fuses visual features, inertial measurements, and UWB range observations within a unified probabilistic framework. Extensive evaluations in both simulation and real-world experiments demonstrate that our approach significantly suppresses trajectory drift and achieves superior localization accuracy and consistency compared to existing tightly coupled VIO and UWB-fusion methods, validating its effectiveness and robustness.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Ultra Wideband (UWB) is widely used to mitigate drift in visual-inertial odometry (VIO) systems. Consistency is crucial for ensuring the estimation accuracy of a UWBaided VIO system. An inconsistent estimator can degrade localization performance, where the inconsistency primarily arises from two main factors: (1) the estimator fails to preserve the correct system observability, and (2) UWB anchor positions are assumed to be known, leading to improper neglect of calibration uncertainty. In this paper, we propose a consistent and tightly-coupled visual-inertial-ranging odometry (CVIRO) system based on the Lie group. Our method incorporates the UWB anchor state into the system state, explicitly accounting for UWB calibration uncertainty and enabling the joint and consistent estimation of both robot and anchor states. Furthermore, observability consistency is ensured by leveraging the invariant error properties of the Lie group. We analytically prove that the CVIRO algorithm naturally maintains the system's correct unobservable subspace, thereby preserving estimation consistency. Extensive simulations and experiments demonstrate that CVIRO achieves superior localization accuracy and consistency compared to existing methods.

Problem

• Mitigating drift in visual-inertial odometry using UWB
• Ensuring consistency in UWB-aided VIO system estimation
• Jointly estimating robot and UWB anchor states consistently

Innovation

• Tightly-coupled visual-inertial-ranging odometry on Lie groups
• Joint estimation of robot and UWB anchor states
• Lie group invariant error ensures observability consistency