A Novel Design Framework for Tightly Coupled IMU/GNSS Sensor Fusion Using Inverse-Kinematics, Symbolic Engines, and Genetic Algorithms

Kourabbaslou, Soroush Sheikhpour; Zhang, Alan L.; Atia, Mohamed

doi:10.1109/jsen.2019.2935324

Cited by 14 publications

(7 citation statements)

References 17 publications

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“…The errors of the accelerometer and gyroscope in inertial navigation (IMU) can be divided into deterministic errors and random errors. Deterministic errors can be obtained through prior calibration, which mainly includes bias errors, scale errors, and axis misalignments [ 21 ]. For random error, it is assumed that the noise obeys Gaussian distribution, including Gaussian white noise and bias random walk.…”

Section: Core Algorithm Design Of Binocular Vision and Interval Navig...mentioning

confidence: 99%

Research on Positioning Method in Underground Complex Environments Based on Fusion of Binocular Vision and IMU

Cheng

Wang

Zhai

et al. 2022

Sensors

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Aiming at the failure of traditional visual slam localization caused by dynamic target interference and weak texture in underground complexes, an effective robot localization scheme was designed in this paper. Firstly, the Harris algorithm with stronger corner detection ability was used, which further improved the ORB (oriented FAST and rotated BRIEF) algorithm of traditional visual slam. Secondly, the non-uniform rational B-splines algorithm was used to transform the discrete data of inertial measurement unit (IMU) into second-order steerable continuous data, and the visual sensor data were fused with IMU data. Finally, the experimental results under the KITTI dataset, EUROC dataset, and a simulated real scene proved that the method used in this paper has the characteristics of stronger robustness, better localization accuracy, small size of hardware equipment, and low power consumption.

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Section: Core Algorithm Design Of Binocular Vision and Interval Navig...mentioning

confidence: 99%

Research on Positioning Method in Underground Complex Environments Based on Fusion of Binocular Vision and IMU

Cheng

Wang

Zhai

et al. 2022

Sensors

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“…To address this challenge, S. S Kourabbaslou [ 22 ] presents a flexible design framework utilizing symbolic engines to represent and linearize system and measurement models. A robust fixed−lag smoothing approach is proposed in case there is a mismatch between the nominal model and the actual model [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

Yin

Zhang

Guo

et al. 2023

Sensors

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High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization.

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“…The integrated navigation system based on inertial navigation system (INS) and global navigation satellite system (GNSS) is one of the most valuable navigation modes, especially the tightly coupled navigation utilizing INS outputs and GNSS raw measurements. [1][2][3][4][5][6] In the navigation mode, even when the currently visible satellites are not enough to perform GNSS calculation alone, the limited GNSS measurement data and INS outputs can still be effectively used to obtain reliable navigation information through state estimation. However, unlike INS/GNSS loosely coupled navigation, due to the nonlinear nature of the GNSS raw measurements, the tightly coupled navigation incorporates nonlinearity in the system, enabling the classical Kalman filter (KF) unsuitable.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel robust progressive cubature Kalman filter with variable step size for inertial navigationsystem/global navigation satellite system tightly coupled navigation

Chen

2023

Adaptive Control & Signal

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Summary This article investigates the state estimation problem of the nonlinear system with the large process prior uncertainty but high‐accuracy measurement information, the situation is prone to occur in the inertial navigation system (INS)/global navigation satellite system (GNSS) tightly coupled navigation system. Furthermore, the unknown heavy‐tailed measurement noises induced by the inaccurate prior navigation information and random measurement outliers are also considered. Given existing methods such as progressive cubature Kalman filter (PCKF) cannot effectively solve the above issues, a novel robust PCKF with variable step size (RVSS‐PCKF) is proposed. First, a new Gaussian‐uniform‐mixing inverse Gamma (GUMIG) distribution is presented to model the variable step size and measurement noise. Next, the GUMIG distribution is expressed as a hierarchical Gaussian presentation and then RVSS‐PCKF is derived with the help of the variational Bayesian (VB) inference. In the filter, the state, variable step size and noise statistic are jointly estimated by the fixed‐point iterations. Finally, the simulations and real data of the tightly coupled navigation illustrate the superiority of the filter in terms of accuracy and steady‐state performance.

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A Novel Design Framework for Tightly Coupled IMU/GNSS Sensor Fusion Using Inverse-Kinematics, Symbolic Engines, and Genetic Algorithms

Cited by 14 publications

References 17 publications

Research on Positioning Method in Underground Complex Environments Based on Fusion of Binocular Vision and IMU

Research on Positioning Method in Underground Complex Environments Based on Fusion of Binocular Vision and IMU

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

A novel robust progressive cubature Kalman filter with variable step size for inertial navigationsystem/global navigation satellite system tightly coupled navigation

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