An Integrated INS/GNSS System With an Attention-Based Deep Network for Drones in GNSS Denied Environments

Taghizadeh, Sina; Safabakhsh, Reza

doi:10.1109/maes.2023.3266180

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…Nevertheless, the errors in INS measurements will accumulate over time and may have a sudden increase affected by the fluctuation or vibration of the transport systems without auxiliary sensors [6]. GNSS has the ability to provide high-precision navigation information and compensates for the accumulation of INS errors through an integrated navigation scheme, but its signals are susceptible to external environmental influence [7]. Affected by occlusion from complex environments, such as high-rise buildings and tunnels, GNSS may become completely ineffective.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Wang,

Zeng,

Shao

et al. 2024

Remote Sensing

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Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.

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Section: Introductionmentioning

confidence: 99%

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Wang,

Zeng,

Shao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

A review of Bayes filters with machine learning techniques and their applications

Kim,

Petrunin,

Shin

2025

Information Fusion

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A robust factor graph optimization method of GNSS/INS/ODO integrated navigation system for autonomous vehicle

Huo,

Jin,

Wang

et al. 2024

Meas. Sci. Technol.

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The GNSS/INS/ODO integrated navigation system provides accurate positioning for autonomous vehicles. However, the observation quality of GNSS and ODO can decrease in complex environments and various driving conditions, leading to a decline in the accuracy and robustness of the integrated navigation system. An FGO-Robust method is proposed to resolve this challenge by fusing the INS/ODO-Slip factor and adaptive GNSS factor. The INS/ODO-Slip factor compensates for ODO measurement values by estimating the wheel slip ratio in real-time during optimization, thereby reducing ODO measurement errors' impact on factor graph optimization. Additionally, the adaptive GNSS factor minimizes the impact of GNSS outliers in the optimization process by designing a dynamic observation weight function. The vehicle experiments are conducted to evaluate the performance of the proposed method in different simulated conditions with ODO and GNSS measurement failures. The experimental results demonstrate that the FGO-Robust method can effectively improve the positioning accuracy of the integrated navigation system under various measurement failure conditions, with improvements of 83.4%, 66.1%, and 68.1% relative to FGO. Moreover, the FGO-Robust method improves the robustness of the integrated navigation system in complex environments and various driving conditions.

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An Integrated INS/GNSS System With an Attention-Based Deep Network for Drones in GNSS Denied Environments

Cited by 5 publications

References 36 publications

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

A review of Bayes filters with machine learning techniques and their applications

A robust factor graph optimization method of GNSS/INS/ODO integrated navigation system for autonomous vehicle

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