A GNSS/INS-integrated system for an arbitrarily mounted land vehicle navigation device

Mu, Mengxue; Zhao, Long

doi:10.1007/s10291-019-0901-8

Cited by 21 publications

(9 citation statements)

References 19 publications

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“…The coordinate frame used here are the VBF (b-frame) with three axes pointing to the front, right, and down (FRD) direction, the inertial sensor frame (m-frame) with three axes pointing to the FRD direction, the inertial frame (i-frame), the navigation frame (n-frame), and the earth-centered earth-fixed frame (e-frame). The definition, transform matrices and illustration of these coordinate frames can be found in [26].…”

Section: Real Time Calibration Algorithmmentioning

confidence: 99%

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Mu¹,

Long²

2022

Meas. Sci. Technol.

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Due to low cost and complementary performance advantages, GNSS/SINS integrated systems have established themselves in certain areas of land vehicle navigation. However, this integrated system cannot maintain reliable positioning solutions in challenging environments due to the inherent fragility of GNSS signals and time accumulated errors of a stand-alone SINS. To address this challenge, a multi-source information fusion system based on the decentralized system architecture and sequential Kalman filter for a land vehicle is proposed, which can fuse information from an odometer and motion aided constraints (MACs) selectively and adaptively in different driving environments. Moreover, a comprehensive calibration and compensation strategy is designed to enhance the information fusion. On the one hand, a real-time calibration algorithm is designed to estimate the time-varying odometer scale factor and the misalignment between the inertial measurement unit (IMU) and vehicle body frame (VBF) when GNSS signals are available. On the other hand, the forward velocity error caused by the lever arm between the origins of the odometer and IMU, and the non-zero lateral velocity generated by turning maneuvers are compensated by the introduced velocity compensation method. A real car experiment in urban areas is carried out to illustrate the effectiveness of the proposed system. It shows that the proposed decentralized GNSS/SINS/odometer fusion system can maintain an average horizontal positioning root mean square error (RMSE) of 1-meter level when GNSS signals are cut off about 1-2 minutes. In addition, compared with the traditional centralized fusion structure, the proposed decentralized fusion structure can mitigate the horizontal positioning RMSE of the whole trajectory from 2.95 m to 0.59 m, which verifies that it can obtain better performance for the application of low-cost sensors in complex GNSS environments.

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Section: Real Time Calibration Algorithmmentioning

confidence: 99%

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Mu¹,

Long²

2022

Meas. Sci. Technol.

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“…Loosely coupled integration has limitations in providing position estimates if the stand-alone GNSS does not produce any position information [1]. On the other hand, loosely coupled integration succeeded in providing a position estimate during a partial outage where there are still 2 to 3 satellites visible (Mu and Zhao, 2019). Unfortunately, in this study, the type of outage that occurred while the car was moving in the basement was a complete outage, as shown in Figure 6, in which no satellites were visible.…”

Section: Discussionmentioning

confidence: 99%

GNSS/IMU Sensor Fusion Performance Comparison of a Car Localization in Urban Environment Using Extended Kalman Filter

Erfianti

Asfihani

Suhandri

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) are popular navigation sensor for position fixing technique and dead reckoning system that complement each other. GNSS can provide accurate position and velocity information when it establishes a Line of Sight (LOS) with a minimum of four satellites. However, this accuracy can decrease due to signal outage, jamming, interference, and multipath effects. On the other hand, the IMU has the advantage of measuring the platform’s orientation with a high-frequency update and is not affected by environmental conditions. However, a drift effect causes the measurement errors to accumulate. Several studies have demonstrated the fusion of both sensors in terms of the Extended Kalman Filter (EKF). This study conduct sensor fusion for car localization in an urban environment based on the loosely coupled integration scheme. In order to improve the sensor fusion performance, pre-processing GNSS and IMU data were applied. The result shows that pre-processing DGNSS and IMU filtering can increase the accuracy of the integrated navigation solution up to 80.02% in the east, 80.13% in the north, and 89.45% in the up direction during the free outage period.

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“…"vertical" represents Z-axis measurements and "horizontal" represents the modulus of X-axis and Y- Considering that the device attitude fluctuation with different holding modes and user walk produces various noise, we perform the coordinate transformation from the carrier coordinate system (b-system) to the navigation coordinate system (n-system) for all sensor data to obtain uniform sensor features, which is free of different holding modes and user walk. The coordinate system transformation from the carrier coordinate system to the navigation coordinate system [33] is shown as follows:…”

Section: Motion Detection Modulementioning

confidence: 99%

Accurate and Robust Floor Positioning in Complex Indoor Environments

Huang

Luo

Shao

et al. 2020

Sensors

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With the widespread development of location-based services, the demand for accurate indoor positioning is getting more and more urgent. Floor positioning, as a prerequisite for indoor positioning in multi-story buildings, is particularly important. Though lots of work has been done on floor positioning, the existing studies on floor positioning in complex multi-story buildings with large hollow areas through multiple floors still cannot meet the application requirements because of low accuracy and robustness. To obtain accurate and robust floor estimation in complex multi-story buildings, we propose a novel floor positioning method, which combines the Wi-Fi based floor positioning (BWFP), the barometric pressure-based floor positioning (BPFP) with HMM and the XGBoost based user motion detection. Extensive experiments show that using our proposed method can achieve 99.2% accuracy, which outperforms other state-of-the-art floor estimation methods.

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A GNSS/INS-integrated system for an arbitrarily mounted land vehicle navigation device

Cited by 21 publications

References 19 publications

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

GNSS/IMU Sensor Fusion Performance Comparison of a Car Localization in Urban Environment Using Extended Kalman Filter

Accurate and Robust Floor Positioning in Complex Indoor Environments

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