Calibration Architecture for the Nonlinear Wheel Odometry Model with Integrated Noise Compensation

Máté Fazekas, Máté Fazekas, Péter Gáspár

2025

Abstract

In the motion estimation of self-driving vehicles, the three main requirements are accuracy, robustness, and cost-effectiveness. The generally applied sensors and methods are the GNSS, inertial, and visual-odometry, but the contradictory requirements demand the integration of new ideas. The wheel odometry could be an adequate choice since the method is robust and cost-effective, but the accuracy of the estimation is limited by the parameter uncertainty, thus a calibration method should be included as well. However, the general parameter identification of a nonlinear model in the presence of noise has not been solved yet. The presented method is based on the assumption that noisy, but several measurements of GNSS and IMU sensors are available in a self-driving vehicle. In the proposed architecture, nonlinear least squares and optimal control techniques are combined in a unique way to compensate for the noise of the orientation and wheel rotation signals to achieve unbiased model calibration. The performance of the developed algorithm and the accuracy of parameter estimation are demonstrated with detailed validation and a test with a real vehicle.

Paper Citation

in Harvard Style

Fazekas M. and Gáspár P. (2025). Calibration Architecture for the Nonlinear Wheel Odometry Model with Integrated Noise Compensation. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 325-332. DOI: 10.5220/0013720800003982

in Bibtex Style

@conference{icinco25,
author={Máté Fazekas and Péter Gáspár},
title={Calibration Architecture for the Nonlinear Wheel Odometry Model with Integrated Noise Compensation},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={325-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013720800003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Calibration Architecture for the Nonlinear Wheel Odometry Model with Integrated Noise Compensation
SN - 978-989-758-770-2
AU - Fazekas M.
AU - Gáspár P.
PY - 2025
SP - 325
EP - 332
DO - 10.5220/0013720800003982
PB - SciTePress