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Deep Learning for Inertial Positioning: A Survey

Changhao Chen, Xianfei Pan

TL;DR

A comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.

Abstract

Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors' measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learning-based techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.

Deep Learning for Inertial Positioning: A Survey

TL;DR

A comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.

Abstract

Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors' measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learning-based techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.
Paper Structure (43 sections, 5 equations, 8 figures, 5 tables)

This paper contains 43 sections, 5 equations, 8 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Taxonomy
  3. Comparison with Other Surveys
  4. Survey Organization
  5. Classical Inertial Navigation Mechanisms
  6. Inertial Measurement Model
  7. Strapdown Inertial Navigation System
  8. Domain Specific Knowledge
  9. Pedestrian Dead Reckoning
  10. Zero-velocity Update
  11. Integrating IMU with Other Sensors
  12. Discussion
  13. Learning to Calibrate Inertial Sensor
  14. Learning to correct IMU Integration
  15. Learning Based IMU Integrated Positioning
  16. ...and 28 more sections

Figures (8)

  • Figure 1: Inertial sensors are ubiquitous in modern platforms such as smartphones, drones, intelligent vehicles, and VR/AR devices. They play a critical role in enabling completely egocentric motion tracking and positioning, making them essential for a range of applications.
  • Figure 2: An overview of our survey structure.
  • Figure 3: An overview of existing deep learning based inertial sensor calibration methods
  • Figure 4: An example of gyro calibration results (reprint from Calib-Net li2022calib). Compared with raw IMU integration, deep learning based calibration models significantly reduce attitude drifts.
  • Figure 5: The velocity of attached platform can be inferred from a sequence of inertial measurements via deep neural networks. (reprint from L-IONet chen2020deep)
  • ...and 3 more figures