Iterative parameter identification method of a vehicle odometry model

Máté Fazekas, Balázs Németh, Péter Gáspár

Research output: Contribution to journalConference article

Abstract

The paper proposes an off-line iterative estimation algorithm for wheel circumference estimation of autonomous vehicles. The motivation of the paper is that the signals of the GPS are not available in parking garages or in several urban areas, e.g. next to the buildings with high walls. Therefore, in these situations the wheel encoder based odometry can be an appropriate choice for autonomous vehicle localization, which requires the precise estimation of the wheel circumference. The proposed novel method has three layers, in which the Kalman-filtering with an enhanced tuning method and the least squares algorithm are performed in an iterative loop. Since the off-line methods uses all of the measurements at once, a highly accurate estimation with low sensitivity on the noise can be reached. The efficiency of the algorithm is presented through CarMaker simulations.

Original languageEnglish
Pages (from-to)199-204
Number of pages6
JournalIFAC-PapersOnLine
Volume52
Issue number15
DOIs
Publication statusPublished - Sep 2019
Event8th IFAC Symposium on Mechatronic Systems, MECHATRONICS 2019 - Vienna, Austria
Duration: Sep 4 2019Sep 6 2019

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Keywords

  • Autonomous vehicle
  • Kalman-filtering
  • Parameter identification
  • Vehicle odometry

ASJC Scopus subject areas

  • Control and Systems Engineering

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