Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?
It addresses the integration of AI with inertial sensing to improve motion estimation in domains such as navigation and unmanned systems, but it is an incremental review paper.
This article reviews how artificial intelligence (AI) can enhance inertial sensing for applications like intelligent transportation, summarizing state-of-the-art methods and challenges based on over 30 representative publications.
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, artificial intelligence (AI) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using AI 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. Based on the over 30 representative articles selected from the nearly 300 related publications, this article summarizes the state of the art, advantages, and challenges on each aspect. Finally, it summarizes nine advantages and nine challenges of AI-enhanced inertial sensing and then points out future research directions.