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<p>This paper presents a trajectory-based calibration method for Ackermann steering vehicles, which models low-level control signals such as wheel encoder pulses and steering wheel angles obtained from production vehicle sensors. The relationship between these signals and vehicle motion is represented using polynomial models. Model parameters are optimized by minimizing trajectory errors with respect to RTK-GPS ground truth. The proposed method is validated through experiments conducted in real-world parking scenarios. The results demonstrate consistent trajectory estimation, with a relative error of 1.22&#xa0;% over the total traveled distance. These findings indicate that the proposed approach provides sufficient accuracy and robustness for motion modeling and low-speed localization in GPS-denied environments.</p>

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Trajectory-Based Calibration of Ackermann Vehicles Using Wheel Encoder Data and Steering Wheel Angles

  • 期刊论文

  • 发表日期：

    2025年7月15日

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  • Hideaki Uchiyama,
  • Kazutaka Hayakawa,
  • Koki Ueda,
  • Yuichi Mizutani

摘要

This paper presents a trajectory-based calibration method for Ackermann steering vehicles, which models low-level control signals such as wheel encoder pulses and steering wheel angles obtained from production vehicle sensors. The relationship between these signals and vehicle motion is represented using polynomial models. Model parameters are optimized by minimizing trajectory errors with respect to RTK-GPS ground truth. The proposed method is validated through experiments conducted in real-world parking scenarios. The results demonstrate consistent trajectory estimation, with a relative error of 1.22 % over the total traveled distance. These findings indicate that the proposed approach provides sufficient accuracy and robustness for motion modeling and low-speed localization in GPS-denied environments.