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

TL;DR

This paper introduces PO-GVINS, a tightly coupled GNSS-visual-inertial positioning framework using a pose-only formulation to improve accuracy and reduce drift in autonomous navigation.

Contribution

It proposes a novel pose-only formulation for visual-inertial navigation integrated with GNSS, reducing linearization errors and computational complexity.

Findings

PO-VINS outperforms MSCKF in accuracy and robustness.

Incorporating GNSS yields drift-free, reliable positioning.

Extensive experiments validate the effectiveness of PO-GVINS.

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.