Paper Info
Title
Inertial Sensing Meets Machine Learning: Opportunity or Challenge?
Abstract
The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, machine learning (ML) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using ML technology to enhance inertial sensing from various aspects, including sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. It summarizes the state of the art, advantages, and challenges on each aspect, and points out future research directions.
Year
DOI
Venue
2022
10.1109/TITS.2021.3097385
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Inertial sensing,artificial intelligence,machine learning,positioning,navigation,location-based service
Journal
23
Issue
ISSN
Citations 
8
1524-9050
0
PageRank 
References 
Authors
0.34
38
7
Name
Order
Citations
PageRank
You Li100.34
Ruizhi Chen240255.33
Xiaoji Niu301.01
Yuan Zhuang400.68
Zhouzheng Gao500.34
Xin Hu6193.68
Naser El-Sheimy700.34