Inertial Sensing Meets Machine Learning: Opportunity or Challenge?

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 t...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-08, Vol.23 (8), p.9995-10011
Main Authors: Li, You, Chen, Ruizhi, Niu, Xiaoji, Zhuang, Yuan, Gao, Zhouzheng, Hu, Xin, El-Sheimy, Naser
Format: Article
Language:English
Subjects:
Algorithms
artificial intelligence
Big Data
Calibration
Data integration
Data models
Inertial navigation
Inertial sensing
Inertial sensing devices
Inertial sensors
Intelligent transportation systems
Location based services
location-based service
Machine learning
Maximum likelihood estimation
Motion simulation
Multisensor fusion
navigation
Navigation systems
positioning
Sensors
Time series analysis
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3097385