WhONet: Wheel Odometry neural Network for vehicular localisation in GNSS-deprived environments

Uche Onyekpe, Vasile Palade, Anuradha Herath, Stratis Kanarachos, Michael E. Fitzpatrick

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
11 Downloads (Pure)

Abstract

In this paper, a deep learning approach is proposed to accurately position wheeled vehicles in Global Navigation Satellite Systems (GNSS) deprived environments. In the absence of GNSS signals, information on the speed of the wheels of a vehicle (or other robots alike), recorded from the wheel encoder, can be used to provide continuous positioning information for the vehicle, through the integration of the vehicle's linear velocity to displacement. However, the displacement estimation from the wheel speed measurements are characterised by uncertainties, which could be manifested as wheel slips or/and changes to the tyre size or pressure, from wet and muddy road drives or tyres wearing out. As such, we exploit recent advances in deep learning to propose the Wheel Odometry neural Network (WhONet) to learn the uncertainties in the wheel speed measurements needed for correction and accurate positioning. The performance of the proposed WhONet is first evaluated on several challenging driving scenarios, such as on roundabouts, sharp cornering, hard-brake and wet roads (drifts). WhONet's performance is then further and extensively evaluated on longer-term GNSS outage scenarios of 30 s, 60 s, 120 s and 180 s duration, respectively over a total distance of 493 km. The experimental results obtained show that the proposed method is able to accurately position the vehicle with up to 93% reduction in the positioning error of its original counterpart (physics model) after any 180 s of travel.

Original languageEnglish
Article number104421
JournalEngineering Applications of Artificial Intelligence
Volume105
Early online date3 Sep 2021
DOIs
Publication statusPublished - Oct 2021

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence, 105, (2021) DOI: 10.1016/j.engappai.2021.104421

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Autonomous vehicles
  • Deep learning
  • GNSS outage
  • Inertial Navigation System
  • Machine learning
  • Neural networks
  • Positioning
  • Wheel odometry

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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