Odometry Model Calibration for Self-Driving Vehicles with Noise Correction
Máté Fazekas, Péter Gáspár, Balázs Németh
Abstract
In the era of self-driving vehicles, state estimation has 3 main contradictory requirements, such as accuracy, robustness, and cost-effectiveness. To satisfy all of them, the integration of the wheel encoder measurements is a proper choice besides the generally applied GNSS, inertial and visual-odometry methods. The wheel odometry is a robust and cost-effective method, but the accuracy of the estimation is limited by the proper knowledge of the vehicle parameter values. However, the calibration of the nonlinear odometry model in the presence of noise remains an open problem in the context of autonomous vehicles yet. This paper presents an algorithm that takes advantage of the assumption that more measurements are available in a self-driving vehicle for accurate parameter estimation. With the proposed architecture, the measurements with distortion effects are detected, and also the noise is corrected to reach unbiased model calibration. The performance of the developed algorithm and the accuracy of the parameter estimation are demonstrated with detailed validation and test with a real vehicle.
BibTeX
@inproceedings{iros2021_odometrymodelcal,
title = {Odometry Model Calibration for Self-Driving Vehicles with Noise Correction},
author = {Máté Fazekas and Péter Gáspár and Balázs Németh},
booktitle = {IROS 2021},
year = {2021}
}