Improved Preintegration Method for GNSS/IMU/In-Vehicle Sensors Navigation Using Graph Optimization

Bai, Shiyu; Lai, Jizhou; Lyu, Pin; Cen, Yiting; Ji, Bowen

doi:10.1109/tvt.2021.3115619

Cited by 19 publications

(13 citation statements)

References 27 publications

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“…The GMM parameter θ used in equation ( 20) is estimated by iteratively alternating between the E-step and the M-step based on the residual sequence o. Based on the pseudorange residual sequence within a sliding window calculated by equation (16), the E-step estimates the hidden variable H. α fj represents the probability of e f belongs to the j Gaussian component. Then the M-step updates the noise parameters based on the hidden variables.…”

Section: Pseudorange Noise Model Estimationmentioning

confidence: 99%

“…Wheel Speed Sensors (WSS), another sensor independent of GNSS and IMU, are also widely installed in vehicles and can be used to improve the integration of GNSS and IMU performance. Existing studies have shown that the integration of GNSS, IMU and WSS promises to provide reliable position for autonomous vehicle navigation [16]. This is because IMU and WSS have the potential to form a reliable and accurate odometry to cover the errors of GNSS.…”

Section: Introductionmentioning

confidence: 99%

“…[19] proposed to integrate the wheel speed with the gyroscope measurements to construct a pre-integration and limit the roll and pitch to zero. Based on this, [16] added acceleration measurements to the pre-integration, where the attitude matrix in the wheel speed pre-integration was calculated from the steering wheel angle. Since all of the above methods were based on the assumption of vehicle planar motion, they were unsuitable for land vehicles traveling outdoors on non-flat ground.…”

Section: Introductionmentioning

confidence: 99%

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FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Zhu,

Zhuo,

Xia

et al. 2024

Meas. Sci. Technol.

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Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled GNSS (Global Navigation Satellite Systems)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the IMU (Inertial Measurement Unit), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a GMM (Gaussian Mixture Model) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the EM (expectation maximization) algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

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Section: Pseudorange Noise Model Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Zhu,

Zhuo,

Xia

et al. 2024

Meas. Sci. Technol.

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“…For example, the researchers (Li et al, 2018a) improve the accuracy of the odometer (which can provide the velocity or mileage of a vehicle) through use of Coriolis effects from three perspectives (scale factor, misalignment and level arm with inertial measurement unit). Multiple sensor calibration (Ali and Mailah, 2019;Bai et al, 2021Bai et al, , 2022 provides the relative pose among multiple different sensors for sensor fusion to improve the accuracy, such as using preintegration theory for IMU (Inertial Measurement Unit) and odometer self-calibration (Bai et al, 2021(Bai et al, , 2022, calibrating gyroscope and magnetometer for data fusion (Ali and Mailah, 2019). However, sensor calibration could slightly reduce the error given the sensor's inherent properties or mounting positions.…”

Section: Related Workmentioning

confidence: 99%

“…Nie et al, 2021;Wang et al, 2022). Additionally, the localization accuracy from SLAM (Simultaneous Localization And Mapping) (Bai et al, 2021(Bai et al, , 2022Grisetti et al, 2007;Hess et al, 2016;Khan et al, 2021;Nubert et al, 2022;Van Nam and Gon-Woo, 2021) is not high enough for precise parking in MMPA. Matching the 2D template marker's images from the teaching and automation stages (Meng et al, 2021) could provide a relative 3D pose for parking error compensation but can only be used to move the robot base on a 2D plane rather than a 3D surface.…”

Section: Introductionmentioning

confidence: 99%

A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments

Pu¹,

Yang²,

Pu³

et al. 2023

Preprint

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To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by harsh industrial environments and the mobile base's parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base's location, environment information, end-effector's path) in the teaching stage for robot learning; letting the end-effector repeat the nearly same path as the reference path in the world frame to reproduce the work in the automation stage. More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking. Then, based on colored point cloud registration, the proposed IPE (Iterative Pose Estimation by Eye & Hand) algorithm could estimate the accurate 6D relative parking pose of the robot arm base without the need of any marker. Finally, the robot could learn the error compensation from the parking pose's bias to modify the end-effector's path to make it repeat a nearly same path in the world coordinate system as recorded in the teaching stage. Hundreds of trials have been conducted with a real mobile manipulator to show the superior robustness of the system and the accuracy of the process automation regardless of the harsh industrial conditions and parking precision. For the released code, please contact

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Vehicle Localization Technique for Traffic Light Advisor Application

Vignarca,

Waitz,

Arrigoni

et al. 2024

Lecture Notes in Mechanical Engineering

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Improved Preintegration Method for GNSS/IMU/In-Vehicle Sensors Navigation Using Graph Optimization

Cited by 19 publications

References 27 publications

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments

Vehicle Localization Technique for Traffic Light Advisor Application

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