Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6dof Inertial Measurement Units}

Golroudbari, Arman Asgharpoor; Sabour, Mohammad Hossein

doi:10.2139/ssrn.4387238

Cited by 3 publications

(7 citation statements)

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“…In these types of inertial navigation system models, automatically discovered features relevant to the task are used. Author [49] proposed an end -to -end deep learning framework to tackle the inertial attitude estimation problem based on IMU measurements.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

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

et al. 2023

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“…Attitude estimation refers to the process of determining the orientation of a robot with respect to a reference frame Asgharpoor Golroudbari (2022) which plays a critical role in the development of autonomous systems. Different types of sensors can be used to perform attitude estimation, including visual, inertial, and GNSS-based sensors Asgharpoor Golroudbari and Sabour (2023). Table 12, presents a brief overview of sensors which can be used in the attitude estimation process.…”

Section: Attitude Estimationmentioning

confidence: 99%

“…To ensure reliable and precise attitude estimation, sensor fusion techniques can be employed to combine the measurements from these sensors to minimize the impact of individual sensor errors Golroudbari and Sabour (2023). There are several techniques for attitude estimation, each with its own strengths and weaknesses.…”

Section: Attitude Estimationmentioning

confidence: 99%

“…A third study utilized a self-attention mechanism, but its model was trained and tested solely on the OxIOD dataset, limiting its applicability to specific motion types and sampling rates Brotchie et al (2022). Asgharpoor et al introduced three end-to-end learning frameworks that aimed to generalize across different environments and sensor sampling rates, although the use of IMU data windows in these frameworks may introduce delays Asgharpoor Golroudbari and Sabour (2023).…”

Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

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Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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This review article presents recent advancements in deep learning method-ologies and applications for autonomous navigation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, obstacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limitations, and areas for further development. The review emphasizes the interdisciplinary nature of autonomous navigation and addresses challenges posed by dynamic and complex environments, uncertainty, and obstacles. With a particular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the importance of navigation in these domains. By synthesizing findings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, recent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable resource for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying , evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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Section: Attitude Estimationmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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This review article presents recent advancements in deep learning methodologies and applications for autonomous nav- igation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, ob- stacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limita- tions, and areas for further development. The review em- phasizes the interdisciplinary nature of autonomous naviga- tion and addresses challenges posed by dynamic and com- plex environments, uncertainty, and obstacles. With a par- ticular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the impor- tance of navigation in these domains. By synthesizing find- ings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, re- cent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable re- source for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying, evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6dof Inertial Measurement Units}

Cited by 3 publications

References 93 publications

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

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

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

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