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

Jun Hu, Xiaoming Lang, Feng Zhang, Yinian Mao, Guoquan Huang

TL;DR

The proposed SRI-GVINS is extensively evaluated on its 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.

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.

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

TL;DR

The proposed SRI-GVINS is extensively evaluated on its 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.

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.
Paper Structure (22 sections, 25 equations, 4 figures, 5 tables)

This paper contains 22 sections, 25 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction and related work
  2. Square-Root Inverse Sliding-Window Filter
  3. IMU Propagation
  4. Marginalization
  5. Measurement Update
  6. SRI-GVINS: Initialization and Update
  7. Frame Initialization
  8. GNSS Receiver Clock Bias
  9. GNSS Measurement Update
  10. Pseudorange Measurement (Psr)
  11. Doppler shift Measurement (Dopp)
  12. Single-differenced Pseudorange Measurement (Dpsr)
  13. Double-differenced Carrier Phase Measurement (Ddcp)
  14. Online GNSS-IMU Calibration
  15. EXPERIMENTS ON UAV DATASETS
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Overview of the proposed SRI-GVINS system.
  • Figure 2: Position errors of SRI-GVINS using different GNSS measurements in the 50m-3km-8m/s dataset.
  • Figure 3: Estimation errors of GNSS-IMU extrinsic calibration and velocity errors in the 60m-1km-6m/s dataset with poor initial GNSS-IMU extrinsic.
  • Figure 4: Trajectories and position errors of each algorithm in the complex_environment dataset.