Velocity estimation is of great importance in autonomous racing. Still, existing solutions are characterized by limited accuracy, especially in the case of aggressive driving or poor generalization to unseen road conditions. To address these issues, we propose to utilize Unscented Kalman Filter (UKF) with a learned dynamics model that is optimized directly for the state estimation task. Moreover, we propose to aid this model with the online-estimated friction coefficient, which increases the estimation accuracy and enables zero-shot adaptation to the new road conditions. To evaluate the UKF-based velocity estimator with the proposed dynamics model, we introduced a publicly available dataset of aggressive manoeuvres performed by an F1TENTH car, with sideslip angles reaching 40{\deg}. Using this dataset, we show that learning the dynamics model through UKF leads to improved estimation performance and that the proposed solution outperforms state-of-the-art learning-based state estimators by 17% in the nominal scenario. Moreover, we present unseen zero-shot adaptation abilities of the proposed method to the new road surface thanks to the use of the proposed learning-based tire dynamics model with online friction estimation.

翻译：速度估计在自主赛车中至关重要。然而，现有解决方案的精度有限，特别是在激进驾驶或对未见道路条件泛化能力较差的情况下。为解决这些问题，我们提出将无迹卡尔曼滤波器（UKF）与一个专为状态估计任务直接优化的学习动力学模型结合使用。此外，我们建议通过在线估计的摩擦系数辅助该模型，这提高了估计精度，并实现了对新道路条件的零样本适应。为评估基于UKF的速度估计器与所提动力学模型的性能，我们引入了一个公开可用的数据集，包含由F1TENTH赛车执行的激进操纵动作，其侧滑角可达40°。利用该数据集，我们表明通过UKF学习动力学模型能提升估计性能，并且所提解决方案在标称场景下优于最先进的基于学习的状态估计器达17%。此外，得益于所提出的基于学习的轮胎动力学模型与在线摩擦估计，我们展示了该方法对新路面的未见零样本适应能力。