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Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions
Robotics · Submitted 22 Jun 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim To encourage further research in integrating deep learning with inertial navigation and fusion and to leverage their capabilities, this paper provides an in-depth review of deep learning methods for inertial sensing and sensor fusion.
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The abstract, sentence by sentence
Inertial sensing is used in many applications and platforms, ranging from day-to-day devices such as smartphones to very complex ones such as autonomous vehicles. In recent years, the development of machine learning and deep learning techniques has increased significantly in the field of inertial sensing and sensor fusion. This is due to the development of efficient computing hardware and the accessibility of publicly available sensor data. These data-driven approaches mainly aim to empower model-based inertial sensing algorithms. To encourage further research in integrating deep learning with inertial navigation and fusion and to leverage their capabilities, this paper provides an in-depth review of deep learning methods for inertial sensing and sensor fusion. We discuss learning methods for calibration and denoising as well as approaches for improving pure inertial navigation and sensor fusion. The latter is done by learning some of the fusion filter parameters. The reviewed approaches are classified by the environment in which the vehicles operate: land, air, and sea. In addition, we analyze trends and future directions in deep learning-based navigation and provide statistical data on commonly used approaches.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Inertial sensing is used in many applications and platforms, ranging from day-to-day devices such as smartphones to very complex ones such as autonomous vehicles. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | In recent years, the development of machine learning and deep learning techniques has increased significantly in the field of inertial sensing and sensor fusion. | background 1.00 | 0.08 | 0.03 | asserted 1.00 |
| s2 | This is due to the development of efficient computing hardware and the accessibility of publicly available sensor data. | background 0.99 | 0.06 | 0.02 | asserted 1.00 |
| s3 | These data-driven approaches mainly aim to empower model-based inertial sensing algorithms. | background 0.96 | 0.07 | 0.04 | asserted 0.97 |
| s4 | To encourage further research in integrating deep learning with inertial navigation and fusion and to leverage their capabilities, this paper provides an in-depth review of deep learning methods for inertial sensing and sensor fusion. | contribution 1.00 | 0.76 | 0.09 | asserted 0.91 |
| s5 | We discuss learning methods for calibration and denoising as well as approaches for improving pure inertial navigation and sensor fusion. | contribution 0.94 | 0.50 | 0.05 | asserted 0.94 |
| s6 | The latter is done by learning some of the fusion filter parameters. | method 0.92 | 0.14 | 0.03 | asserted 0.99 |
| s7 | The reviewed approaches are classified by the environment in which the vehicles operate: land, air, and sea. | method 0.65 | 0.84 | 0.03 | asserted 0.97 |
| s8 | In addition, we analyze trends and future directions in deep learning-based navigation and provide statistical data on commonly used approaches. | contribution 0.90 | 0.86 | 0.13 | asserted 0.97 |
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The paper
Original abstract
Inertial sensing is used in many applications and platforms, ranging from day-to-day devices such as smartphones to very complex ones such as autonomous vehicles. In recent years, the development of machine learning and deep learning techniques has increased significantly in the field of inertial sensing and sensor fusion. This is due to the development of efficient computing hardware and the accessibility of publicly available sensor data. These data-driven approaches mainly aim to empower model-based inertial sensing algorithms. To encourage further research in integrating deep learning with inertial navigation and fusion and to leverage their capabilities, this paper provides an in-depth review of deep learning methods for inertial sensing and sensor fusion. We discuss learning methods for calibration and denoising as well as approaches for improving pure inertial navigation and sensor fusion. The latter is done by learning some of the fusion filter parameters. The reviewed approaches are classified by the environment in which the vehicles operate: land, air, and sea. In addition, we analyze trends and future directions in deep learning-based navigation and provide statistical data on commonly used approaches.
Human review
References
How the literature uses it
9 arXiv papers cite this in 9 sentences
Papers citing it · 4 sentences without a confident label
Cites it as background 5 papers
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Furthermore, recent machine learning and deep learning approaches have further advanced performance in various navigation tasks [ref] .
Underwater MEMS Gyrocompassing: A Virtual Testing Ground · Introduction · 0.84
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As hardware capabilities advanced to handle heavy computational loads, the utilization of machine learning (ML) and deep learning (DL) techniques emerged to enhance AUV navigation [ref] .
Data-Driven Strategies for Coping with Incomplete DVL Measurements · Introduction · 0.90
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These approaches are used in a variety of navigation tasks [ref] , including PDR [ref] , as described below.
Pedestrian Inertial Navigation: An Overview of Model and Data-Driven Approaches · Data-Driven Pedestrian Dead Reckoning · 0.80
What it cites · 8 sentences without a confident label
Cites as background 4 papers
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A survey was conducted in [ref] to examine inertial positioning using recent DL methods.
Deep Learning for Inertial Positioning: A Survey · Introduction · 0.86
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In [ref] there is a discussion of end-to-end DL methods employed in autonomous navigation, including subjects other than navigation such as obstacle detection, scene perception, path planning, and control.
Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review · Introduction · 0.85
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In light of DL's significant advances in the fields of image processing and computer vision, vision-based navigation surveys were conducted for different platforms and in general [ref] .
From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN) · Introduction · 0.83
Discussion
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