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Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology

Chen, Hua
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1651081183600888

Abstract Details

2022, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical and Computer Engineering.
Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including Global Navigation Satellite System (GNSS)- denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position and velocity information using mechanization equations. In this work, we developed a novel deep learning-based methodology, using Convolutional Neural Networks (CNN) to reduce errors from MEMS IMU sensors. We developed a methodology of using CNN algorithms that can learn from the responses of a particular inertial sensor while subject to inherent noise errors and provide a near real-time error correction. We implemented a time-division method to divide the IMU output data into small step sizes. By using this method, we make the IMU outputs fit the input format of the CNN. We optimized the CNN algorithm for higher performance and lower complexity that would allow its implementation on ultra-low power hardware such as microcontrollers. We examined the performance of our CNN algorithm under various situations with IMUs of various performance grades, IMUs of the same type but different manufactured batch, and controlled, fixed and un-controlled vehicle motion paths.
Vamsy Chodavarapu (Committee Chair)
Manish Kumar (Committee Member)
Guru Subramanyam (Committee Member)
Tarek Taha (Committee Member)
65 p.
Electrical Engineering
Inertial navigation; Inertial measurement unit; MEMS IMU; Deep learning; Autonomous driving

Recommended Citations

Citations

  • Chen, H. (2022). Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1651081183600888

    APA Style (7th edition)

  • Chen, Hua. Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology. 2022. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1651081183600888.

    MLA Style (8th edition)

  • Chen, Hua. "Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology." Doctoral dissertation, University of Dayton, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1651081183600888

    Chicago Manual of Style (17th edition)

dayton1651081183600888
542
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This open access ETD is published by University of Dayton and OhioLINK.