Precise and robust sideslip angle estimation based on INS/GNSS integration using invariant extended Kalman filter

Zhang, Zhihuang; Zhao, Jintao; Huang, Changyao; Li, Liang

doi:10.1177/09544070221102662

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…Furthermore, Xia et al investigate the stability of the proposed sideslip angle estimation algorithm [43]. Zhang et al present an innovative VKB method for sideslip angle estimation [103], which continuously estimates the attitude and velocity of the vehicle to obtain the sideslip angle. Within the INS-based framework, the sideslip angle is the most difficult to estimate due to the observability issue [46].…”

Section: B Gau Approachmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and indepth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

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Section: B Gau Approachmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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“…The proposed framework gave rise to a class of invariant filters that rapidly became state-of-the-art for positioning problems. Invariant Kalman filters (IKFs) have been applied to different mobile platforms such as autonomous underwater vehicles (AUVs) [23], quadcopters [24], bipedal robots [25], aircrafts [26], and cars [27]. The properties of IKFs helped to solve various challenges in mobile navigation, such as Simultaneous Localization and Mapping (SLAM) [28] and visual-inertial odometry [29].…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Land Vehicle Positioning: Invariant Kalman Filters and Deep-Learning-Based Radar Velocity Estimation

Araujo¹,

Elhabiby²,

Givigi³

et al. 2023

Preprint

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<p>Autonomous and intelligent vehicles are multi-sensor systems operating in various environments and conditions. Due to their characteristics, inertial measurement units (IMUs) are typically the core component of such systems. However, these sensors rapidly accumulate errors due to biases and noise, degrading the positioning solution. Therefore, this paper presents a positioning solution that only uses three gyroscopes and one radar. The proposed method was tested using low-cost sensors in different scenarios, such as open-sky, urban and indoor areas. The key components of the method are the invariant Kalman filters and the use of deep neural networks to estimate the forward velocity of the car using the radar readings. The method was tested on a custom dataset, and our integrated solution accurately estimates the vehicle's 3D position, velocity, and orientation. We achieved, on average, a 1.45% translational error in the tested scenarios, making the proposed method a robust alternative to current IMU-based positioning methods.</p>

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“…Inspired by vehicle kinematics, a novel method of heading angle estimation based on Zero-Heading angle-Variation-Constraint and Sequential-Adaptive Unscented Kalman Filter algorithms is proposed for solving the problem of heading angle unobservability and the instability of the filtering [23]. Owing to the presence of heading angular outputs from dual-antenna GNSS and INS systems, the mounting angle errors will reduce the heading angle accuracy [24]. GNSS antennas usually have a regular shape, and the mounting angle can be measured using optical methods.…”

Section: Introductionmentioning

confidence: 99%

Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman Filter

Jiao,

Xu,

Chen

et al. 2024

Sensors

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High-accuracy heading angle is significant for estimating autonomous vehicle attitude. By integrating GNSS (Global Navigation Satellite System) dual antennas, INS (Inertial Navigation System), and a barometer, a GNSS/INS/Barometer fusion method is proposed to improve vehicle heading angle accuracy. An adaptive Kalman filter (AKF) is designed to fuse the INS error and the GNSS measurement. A random sample consensus (RANSAC) method is proposed to improve the initial heading angle accuracy applied to the INS update. The GNSS heading angle obtained by a dual-antenna orientation algorithm is additionally augmented to the measurement variable. Furthermore, the kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to enhance the accuracy of the measurement model. The heading errors in the open and occluded environment are 0.5418° (RMS) and 0.636° (RMS), which represent reductions of 37.62% and 47.37% compared to the extended Kalman filter (EKF) method, respectively. The experimental results demonstrate that the proposed method effectively improves the vehicle heading angle accuracy.

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Precise and robust sideslip angle estimation based on INS/GNSS integration using invariant extended Kalman filter

Cited by 9 publications

References 32 publications

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

A Novel Method for Land Vehicle Positioning: Invariant Kalman Filters and Deep-Learning-Based Radar Velocity Estimation

Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman Filter

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