Machine Learning Boosting Algorithms for Determining Euler Angles in an Inertial Navigation System

Grekov, A.N.; Kabanov, Aleksey

doi:10.1109/rusautocon54946.2022.9896248

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…As for all the scientific and industrial fields and applications, also in inertial and integrated navigation, several papers have been presented in order to investigate the advantages brought by the exploitation of AI techniques [29]. In particular, Machine Learning (ML) has been adopted to enhance inertial sensor performances at different stages of their typical application fields, from a fundamental hardware level (e.g., gyros lifecycle estimation [32]) to calibration and error modeling (e.g., ANN for thermal drift compensation [33]), from inertial navigation (e.g., a machine-learning algorithm for Euler angle measurements [34]) to high-level applications (e.g., action classification based on IMU by means of ANN [35]).…”

Section: Artificial Intelligence For Inertial Sensingmentioning

confidence: 99%

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

Alteriis

Ruggiero

Prete

et al. 2023

Sensors

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In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models’ performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters.

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Section: Artificial Intelligence For Inertial Sensingmentioning

confidence: 99%

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

Alteriis

Ruggiero

Prete

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

A Hybrid CNN-LSTM-Based Approach for Pedestrian Dead Reckoning Using Multi-Sensor-Equipped Backpack

et al. 2023

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Researchers in academics and companies working on location-based services (LBS) are paying close attention to indoor localization based on pedestrian dead reckoning (PDR) because of its infrastructure-free localization method. PDR is the fundamental localization technique that utilize human motion to perform localization in a relative sense with respect to the initial position. The size, weight, and power consumption of micromechanical systems (MEMS) embedded into smartphones are remarkably low, making them appropriate for localization and positioning. Traditional pedestrian PDR methods predict position and orientation using stride length and continuous integration of acceleration in step and heading system (SHS)-based PDR and inertial navigation system (INS)-PDR, respectively. However, these two approaches provide accumulations of error and do not effectively leverage the inertial measurement unit (IMU) sequences. The PDR navigation solution relays on the standard of the MEMS, which yields PDR with the acceleration and angular velocity from the accelerometer and gyroscope, respectively. However, low-cost small MEMSs endure enormous error sources such as bias and noise. Hence, MEMS assessments lead to navigation solution drifts when utilized as inputs to the PDR. As a consequence, numerous methods have been proposed to mitigate and model the errors related to MEMS. Deep learning-based dead reckoning algorithms are provided to address aforementioned issues owing to the end-to-end learning framework. This paper proposes a hybrid convolutional neural network (CNN) and long short-term memory network (LSTM)-based inertial PDR system that extracts inertial measurement units (IMU) sequence features. The end-to-end learning framework is introduced to leverage the efficiency of low-cost MEMS because data-driven solutions provide more complete knowledge of the ever-increasing data volume and computational power over the filtering model approach. A CNN-LSTM model was employed to capture local spatial and temporal features. Experiments conducted on odometry datasets collected from multi-sensor backpack devices demonstrated that the proposed architecture outperformed previous traditional PDR methods, demonstrating that the root mean square error (RMSE) for the best user was 0.52 m. On the handheld smartphone-only dataset the best achieved R2 metric was 0.49.

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Machine Learning Boosting Algorithms for Determining Euler Angles in an Inertial Navigation System

Cited by 2 publications

References 17 publications

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

A Hybrid CNN-LSTM-Based Approach for Pedestrian Dead Reckoning Using Multi-Sensor-Equipped Backpack

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