Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions

• URL: http://arxiv.org/abs/2307.00014v2
• Date: Sun, 25 Feb 2024 16:24:09 GMT
• Title: Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions
• Authors: Nadav Cohen and Itzik Klein
• Abstract summary: 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 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.
• Score: 15.619053656143564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond [58.63558696061679]
  Trajectory computing is crucial in various practical applications such as location services, urban traffic, and public safety. We present a review of development and recent advances in deep learning for trajectory computing (DL4Traj) Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold potential to augment trajectory computing.
  arXiv  Detail & Related papers  (2024-03-21T05:57:27Z)
• Federated Learning on Edge Sensing Devices: A Review [0.0]
  Federated Learning (FL) is emerging as a solution to privacy, hardware, and connectivity limitations. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized.
  arXiv  Detail & Related papers  (2023-11-02T12:55:26Z)
• End-to-end Autonomous Driving using Deep Learning: A Systematic Review [0.0]
  End-to-end autonomous driving is a fully differentiable machine learning system that takes raw sensor input data and other metadata as prior information and directly outputs the ego vehicle's control signals or planned trajectories. This paper attempts to systematically review all recent Machine Learning-based techniques to perform this end-to-end task, including, but not limited to, object detection, semantic scene understanding, object tracking, trajectory predictions, trajectory planning, vehicle control, social behavior, and communications.
  arXiv  Detail & Related papers  (2023-08-27T17:43:58Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Deep Learning for Inertial Positioning: A Survey [4.188058836787458]
  Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. Deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. 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.
  arXiv  Detail & Related papers  (2023-03-07T09:33:49Z)
• Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving [58.879758550901364]
  Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context. We introduce a framework that integrates three cameras to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset.
  arXiv  Detail & Related papers  (2022-10-13T05:56:20Z)
• Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge? [12.244109673209769]
  This article reviews the research on using AI technology to enhance inertial sensing from various aspects. It includes sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. It summarizes nine advantages and nine challenges of AI-enhanced inertial sensing and then points out future research directions.
  arXiv  Detail & Related papers  (2020-07-13T22:33:21Z)
• A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence [48.67755344239951]
  We provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping. It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities.
  arXiv  Detail & Related papers  (2020-06-22T19:01:21Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.