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

TL;DR

CVIRO introduces a Lie group-based visual-inertial-ranging odometry system that jointly estimates robot and anchor states, ensuring consistency and improved localization accuracy by accounting for calibration uncertainties.

Contribution

The paper presents a novel consistent and tightly-coupled VIO system that explicitly models UWB anchor uncertainties and preserves system observability using Lie group properties.

Findings

CVIRO outperforms existing methods in localization accuracy.

The system maintains estimation consistency through invariant error properties.

Extensive experiments validate the effectiveness of the proposed approach.

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.