A Tightly Coupled and Invariant Filter for Visual-Inertial-GNSS-Barometer Odometry

Abstract

A positioning system that relies solely on observations based on a local frame, such as visual-inertial odometry (VIO), suffers from unobservable directions, leading to cumulative estimation errors over time. To address this issue, we propose a tightly-coupled Visual-Inertial-GNSS-Barometer odometry (GBVIO) based on an invariant filter. The integration of Global Navigation Satellite System (GNSS) and barometric data enables global convergence. Our system supports both tightly coupled updates (using pseudorange and Doppler shift measurements) and loosely coupled updates (using global position data). We prove that the invariant filter using barometric observations remains invariant under stochastic unobservable transformations, thus exhibiting improved consistency. Validation through computer-based Monte Carlo simulations and real-world dataset experiments demonstrates the superiority of GBVIO over standalone VIO. All source code and datasets are publicly available.