Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

Klein, Itzik

doi:10.1109/iss55898.2022.9926294

Cited by 10 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent studies have used IMU data to train neural networks to learn motion models and output velocity estimates directly from IMU measurements. With the use of body wearable IMU, data-driven models [35] and filtering models [36] have been implemented into several positioning and navigation fields of applications.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Among the latest research directions on FE, data-driven terrain navigation is increasingly becoming a hot topic of interest. This approach takes data as the input of the model, learns from the data, and optimizes the model in continuous iterations, which makes the FE capability of the model increase and gradually converge the model to the realistic state [69] . The process schematic is shown in Figure 7.…”

Section: ) Feature Extractionmentioning

confidence: 99%

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

et al. 2023

View full text Add to dashboard Cite

The prerequisite for underwater vehicles to accomplish various underwater tasks is to obtain real-time position information about themselves in the underwater environment using internal or external sensors, which is known as underwater positioning and navigation technology. This paper presents a general review of underwater vehicle positioning and navigation technology and illustrates the importance of underwater terrain-aided navigation technology. It also summarizes its general structure and describes the principles and functions of each component. The paper mainly analyzes various mainstream methods applied to underwater terrain-aided navigation, focuses on terrain suitability analysis and terrain matching algorithms, which play a central role in the analysis. Additionally, the research progress of terrain-aided navigation technology is described, and the research methods of terrain suitability analysis, terrain matching navigation, and the latest technology applications are compared. Some shortcomings of the current underwater terrain-aided navigation technology are presented, and future research directions are foreseen to provide a basis for subsequent research.

show abstract

MissBeamNet: learning missing Doppler velocity log beam measurements

Yona,

Klein

2023

Neural Comput & Applic

View full text Add to dashboard Cite

Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

Cited by 10 publications

References 33 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

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

MissBeamNet: learning missing Doppler velocity log beam measurements

Contact Info

Product

Resources

About