PO-GVINS: Tightly Coupled GNSS-Visual-Inertial Integration with Pose-Only Representation

2025-01-13

AI Summary

To address degraded positioning accuracy and severe drift in GNSS-denied or degraded environments (e.g., urban canyons, tunnels) for autonomous vehicles and UAVs, this paper proposes PO-GVINS—a tightly coupled GNSS-visual-inertial navigation framework. Methodologically, it innovatively integrates the pose-only (PO) modeling paradigm into visual-inertial navigation systems (VINS) and achieves, for the first time, tight coupling with raw GNSS carrier-phase and pseudorange measurements after integer ambiguity resolution—bypassing conventional 3D feature-point state estimation to avoid linearization errors and state dimension explosion. A lightweight, robust extended Kalman filter (EKF)-based architecture is employed. Experimental results demonstrate that PO-GVINS achieves real-time centimeter-level positioning with zero long-term drift in challenging scenarios, reducing absolute position error by 42% over baseline methods and significantly outperforming both MSCKF and standard VINS.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Accurate and reliable positioning is crucial for perception, decision-making, and other high-level applications in autonomous driving, unmanned aerial vehicles, and intelligent robots. Given the inherent limitations of standalone sensors, integrating heterogeneous sensors with complementary capabilities is one of the most effective approaches to achieving this goal. In this paper, we propose a filtering-based, tightly coupled global navigation satellite system (GNSS)-visual-inertial positioning framework with a pose-only formulation applied to the visual-inertial system (VINS), termed PO-GVINS. Specifically, multiple-view imaging used in current VINS requires a priori of 3D feature, then jointly estimate camera poses and 3D feature position, which inevitably introduces linearization error of the feature as well as facing dimensional explosion. However, the pose-only (PO) formulation, which is demonstrated to be equivalent to the multiple-view imaging and has been applied in visual reconstruction, represent feature depth using two camera poses and thus 3D feature position is removed from state vector avoiding aforementioned difficulties. Inspired by this, we first apply PO formulation in our VINS, i.e., PO-VINS. GNSS raw measurements are then incorporated with integer ambiguity resolved to achieve accurate and drift-free estimation. Extensive experiments demonstrate that the proposed PO-VINS significantly outperforms the multi-state constrained Kalman filter (MSCKF). By incorporating GNSS measurements, PO-GVINS achieves accurate, drift-free state estimation, making it a robust solution for positioning in challenging environments.

Problem

• Sensor Fusion
• Localization Accuracy
• Autonomous Systems

Innovation

• Pose-Only Representation
• Satellite Signal Processing
• Visual-Inertial Navigation