Learning Uncertainties in Wheel Odometry for Vehicular Localisation in GNSS Deprived Environments

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

Inertial Navigation Systems (INS) are commonly used to localise vehicles in the absence of Global Navigation Satellite Systems (GNSS) signals. However, they are plagued by noises, which grow exponentially over time during the triple integration computation, leading to a poor navigation solution. We explore the wheel encoder as an alternative to the accelerometer of the INS for positional tracking, and for the first time investigate the capability of deep learning using the Long Short-Term Memory (LSTM) neural network to learn the uncertainty inherent in the wheel speed measurements. These uncertainties could be manifested as changes in the tyre size or pressure, or wheel slips as a result of worn out tyres or wet/muddy road drive. The proposed solution has less integration steps in its computation, therefore providing the potential for a more accurate positioning estimation. Through a performance evaluation on several challenging scenarios for vehicular driving, such as hard braking, quick changes in vehicular acceleration, and wet/muddy road driving, we show that the wheel speed-based positioning approach is able to achieve up to 81.46 % improvement compared to the INS accelerometer approach.

Original languageEnglish
Title of host publication2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
EditorsM. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi
PublisherIEEE
Pages741-746
Number of pages6
ISBN (Electronic)978-1-7281-8470-8
ISBN (Print)978-1-7281-8471-5
DOIs
Publication statusPublished - Feb 2021
EventIEEE 19th International Conference on Machine Learning Applications - Miami, United States
Duration: 14 Dec 202016 Dec 2020
Conference number: 19

Publication series

NameProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

Conference

ConferenceIEEE 19th International Conference on Machine Learning Applications
Abbreviated titleICMLA
CountryUnited States
CityMiami
Period14/12/2016/12/20

Keywords

  • Autonomous Vehicle Navigation
  • Deep learning
  • GPS outage
  • Inertial Navigation System (INS)
  • LSTM
  • Wheel Speed

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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