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Research ArticleOriginal Article
Open Access

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

Weisong Wen, Tim Pfeifer, Xiwei Bai and Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation June 2021, 68 (2) 315-331; DOI: https://doi.org/10.1002/navi.421
Weisong Wen
1Hong Kong Polytechnic University, Hung Hom, Hong Kong
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Tim Pfeifer
2Chemnitz University of Technology, Chemnitz, Germany
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  • For correspondence: [email protected]
Xiwei Bai
1Hong Kong Polytechnic University, Hung Hom, Hong Kong
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Li-Ta Hsu
1Hong Kong Polytechnic University, Hung Hom, Hong Kong
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  • For correspondence: [email protected]
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  • FIGURE 1
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    FIGURE 1

    Flowchart of the loosely coupled (blue line) and tightly coupled (red line) GNSS-INS integrations implemented using the EKF

  • FIGURE 2
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    FIGURE 2

    Illustration of the graph structure of the implemented loosely coupled and tightly coupled Global Navigation Satellite System–inertial navigation system integrations using factor graph optimization

  • FIGURE 3
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    FIGURE 3

    The left image shows the experimental vehicle and sensor setup, and the right image illustrates the tested urban canyon in Hong Kong

  • FIGURE 4
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    FIGURE 4

    Trajectories of the LC GNSS/INS integrations using EKF and FGO in the east, north, and up (ENU) frame. The black curve denotes the reference trajectory. The red and blue curves in the left-hand side figure represent the trajectories from LC integrations using EKF and FGO, respectively. The right figure shows the 2D error

  • FIGURE 5
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    FIGURE 5

    Trajectories of TC GNSS/INS integrations using EKF and FGO in the east, north, and up (ENU) frame. The black curve denotes the reference trajectory. The red and blue curves in the left-hand side figure represent the trajectories from TC integrations using EKF and FGO, respectively. The right figure shows the 2D error

  • FIGURE 6
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    FIGURE 6

    2D residuals of the loosely coupled GNSS/INS integrations using EKF and FGO

  • FIGURE 7
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    FIGURE 7

    2D residuals of the tightly coupled GNSS/INS integrations using EKF and FGO

  • FIGURE 8
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    FIGURE 8

    2D positioning errors under different window sizes used in TC GNSS/INS integrations using FGO. The x-axis denotes the epochs, and the y-axis represents the value of 2D positioning errors. The red and green rectangles denote the sliding windows of sizes 30 and 256 s, respectively

  • FIGURE 9
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    FIGURE 9

    Sky-view and satellite visibilities of the four selected epochs in Figure 8. The blue and red circles denote the line-of-sight (LOS) and non-line-of-sight (NLOS) satellites, respectively

  • FIGURE 10
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    FIGURE 10

    Histogram of GPS (left) and BeiDou (right) pseudorange errors and the corresponding fitted Gaussian mixture model (GMM) inside a sliding window of epoch D with a size of 30 s (as indicated by the red rectangle in Figure 8). The x-axis denotes the value of pseudorange errors acquired by the ray-tracing technique. The y-axis denotes the counts of errors within the histogram. The blue curve represents the GMM fitted by the histogram using three Gaussian components

  • FIGURE 11
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    FIGURE 11

    Histogram and Gaussian mixture models (GMMs) of pseudorange residuals near epoch D with a sliding window of 30 s. These are similar to Figure 10, with the major difference being that the histogram and GMMs are based on the residuals

  • FIGURE 12
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    FIGURE 12

    Histogram and Gaussian mixture models (GMMs) of pseudorange residuals similar to Figure 11, with the major difference being that the histogram and GMMs are based on the residuals near epoch D with a sliding window of 256 s

  • FIGURE 13
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    FIGURE 13

    Comparison of the computational time used in tightly coupled (TC) integration using factor graph optimization (FGO) and the TC incremental smoothing and mapping algorithm (iSAM) (Kaess et al., 2008)

Tables

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    TABLE 1

    Parameter values used in this paper

    ParameterFTA
    Value104530
    ParameterSUERE (m)Embedded ImageEmbedded Image
    Value108.17e-31.31e-4
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    TABLE 2

    Positioning performance and computational load (time) of the four methods

    All dataLoosely coupled EKFTightly coupled EKFLoosely coupled FGOTightly coupled FGO
    Mean error9.14 m8.03 m7.01 m3.64 m
    Std7.60 m7.15 m6.41 m2.84 m
    Used Time0.053 s0.071 s15.41 s75.30 s

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NAVIGATION: Journal of the Institute of Navigation: 68 (2)
NAVIGATION: Journal of the Institute of Navigation
Vol. 68, Issue 2
Summer 2021
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Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter
Weisong Wen, Tim Pfeifer, Xiwei Bai, Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation Jun 2021, 68 (2) 315-331; DOI: 10.1002/navi.421

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Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter
Weisong Wen, Tim Pfeifer, Xiwei Bai, Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation Jun 2021, 68 (2) 315-331; DOI: 10.1002/navi.421
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    • Abstract
    • 1 INTRODUCTION
    • 2 RELATED WORK
    • 3 METHODOLOGY
    • 4 EXPERIMENT EVALUATION
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Keywords

  • extended Kalman filter
  • factor graph optimization
  • GNSS
  • INS
  • integration
  • navigation
  • positioning
  • urban canyons
  • window size

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