Square-Root Inverse Filter-based GNSS-Visual-Inertial Navigation

TL;DR

This paper introduces SRI-GVINS, a novel tightly coupled GNSS-Visual-Inertial Navigation System that fuses multiple GNSS measurements with visual-inertial data within a square-root inverse filtering framework, enhancing stability and efficiency.

Contribution

The paper presents a new SRI-GVINS system that deeply fuses diverse GNSS measurements with visual-inertial data using a square-root inverse filtering approach, and includes online calibration and sequential initialization.

Findings

Suppresses VIO drift in real-time

Outperforms state-of-the-art methods in accuracy

Demonstrates computational efficiency and stability

Abstract

While Global Navigation Satellite System (GNSS) is often used to provide global positioning if available, its intermittency and/or inaccuracy calls for fusion with other sensors. In this paper, we develop a novel GNSS-Visual-Inertial Navigation System (GVINS) that fuses visual, inertial, and raw GNSS measurements within the square-root inverse sliding window filtering (SRI-SWF) framework in a tightly coupled fashion, which thus is termed SRI-GVINS. In particular, for the first time, we deeply fuse the GNSS pseudorange, Doppler shift, single-differenced pseudorange, and double-differenced carrier phase measurements, along with the visual-inertial measurements. Inherited from the SRI-SWF, the proposed SRI-GVINS gains significant numerical stability and computational efficiency over the start-of-the-art methods. Additionally, we propose to use a filter to sequentially initialize the reference frame transformation till converges, rather than collecting measurements for batch optimization. We also perform online calibration of GNSS-IMU extrinsic parameters to mitigate the possible extrinsic parameter degradation. The proposed SRI-GVINS is extensively evaluated on our own collected UAV datasets and the results demonstrate that the proposed method is able to suppress VIO drift in real-time and also show the effectiveness of online GNSS-IMU extrinsic calibration. The experimental validation on the public datasets further reveals that the proposed SRI-GVINS outperforms the state-of-the-art methods in terms of both accuracy and efficiency.