A Robust GNSS/PDR Integration Scheme with GRU-Based Zero-Velocity Detection for Mass-Pedestrians

Xie, Dongpeng; Jiang, Jinguang; Wu, Jiaji; Yan, Peihui; Tang, Yanan; Zhang, Chao; Liu, Jingnan

doi:10.3390/rs14020300

Cited by 11 publications

(6 citation statements)

References 39 publications

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“…However, studies like [33] predict absolute state vectors instead of vector increments using NN, which increases model complexity and requires a more extensive training process. Studies from [29,[32][33][34][35][36][37][38][39][40][41][42][43][44][45] adopted vector increments of the sensor observations and predictions during KF prediction, whilst most of the work only works on GNSS/INS navigation during GNSS outages, aiming for improving INS efficiency INS in urban settings and situations [31,38,39,41,42,46].…”

Section: Hybrid Fusion Enhanced By Aimentioning

confidence: 99%

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

Tabassum,

Xu,

Petrunin

et al. 2023

Aerospace

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To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors.

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Section: Hybrid Fusion Enhanced By Aimentioning

confidence: 99%

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

Tabassum,

Xu,

Petrunin

et al. 2023

Aerospace

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show abstract

“…FGO, on the other hand, is a graphical representation of a probabilistic model that is used for inference in Bayesian networks. In addition to GNSS and PDR, other measurements such as ZUPT (Xie et al, 2022), Bluetooth, and Wi-Fi can also be integrated into GNSS/PDR fusion to further improve positioning accuracy and reliability. ZUPT is a method that utilizes the knowledge of a stationary period to reset the velocity error in PDR.…”

Section: Gnss/pdr Integrationmentioning

confidence: 99%

A CNN-Speed-Based GNSS/PDR Integrated System for Smartwatch

Lin

Huang

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In recent years, wearable devices such as smart bands and smartwatches have gained widespread popularity due to their ability to provide various health and fitness applications by detecting and analyzing the human body and motion information. However, the accuracy of location-based services can be limited, especially in urban areas and indoors. This study proposes a series of smartwatch Pedestrian Dead Reckoning (PDR) improvements based on 9 Degrees of Freedom (DOF) IMU orientation estimation, which includes the heading estimation of human movement and a novel pre-trained velocity regression model. The proposed system holds the potential to enhance positioning accuracy and augment navigation availability for smartwatch users, thus offering potential applications across various fields. This study makes significant contributions to the field of smartwatch navigation by proposing a GNSS/PDR fusion algorithm specifically designed for the consumer-grade IMU, magnetometer, and GNSS receiver built into Apple Watch, tracking varied roll and pitch of the sensor caused by hand swing, and integrating a CNN model to predict the 1-D speed and provide ZUPT information, offering improved accuracy and reliability.

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“…Generally, PD/GNSS integration is constructed with the positions from the PDR and GNSS. However, GNSS is not always available [8], [9], [10]. How to improve the PDR accuracy without GNSS has attracted wide attention.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Smartphone GNSS/PDR for Pedestrian Navigation

Jiang

Chen

et al. 2023

IEEE Trans. Circuits Syst. II

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Pedestrian navigation using smartphone built-in sensors attracted wide attention with the booming Location-based Service (LBS). Pedestrian Dead Reckoning (PDR) and Global Navigation Satellite Navigation (GNSS) integration is recognized as a reliable solution for smartphone-based pedestrian navigation. However, GNSS is vulnerable under some conditions. Multi-path, None-Line-Of-Sight (NLOS), and signal blockage all pose negative impacts on GNSS position accuracy. PDR position errors increase with the pedestrian walking distance without GNSS. Aiming at improving the smartphone-based pedestrian position accuracy under GNSS signal challenging conditions, in this brief, we propose a cooperative PDR/GNSS integration method with Factor Graph Optimization (FGO). A factor graph is constructed to represent the relationship between the multiple agents' states, measurements and inter-ranging information. Optimal estimation is realized considering all the historical measurements and interranging measurements between these agents. Field tests were carried out to assess the performance of the proposed cooperative navigation method. Results manifest that the proposed method can improve the position accuracy especially under the GNSS signals challenging conditions.

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A Robust GNSS/PDR Integration Scheme with GRU-Based Zero-Velocity Detection for Mass-Pedestrians

Cited by 11 publications

References 39 publications

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

A CNN-Speed-Based GNSS/PDR Integrated System for Smartwatch

Cooperative Smartphone GNSS/PDR for Pedestrian Navigation

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