Inertial odometry (IO) relies exclusively on signals from an inertial measurement unit (IMU) for localization and offers a promising avenue for consumer grade positioning. However, accurate modeling of the nonlinear motion patterns present in IMU signals remains the principal limitation on IO accuracy. To address this challenge, we propose CKANIO, an IO framework that integrates Chebyshev based Kolmogorov-Arnold Networks (Chebyshev KAN). Specifically, we design a novel residual architecture that leverages the nonlinear approximation capabilities of Chebyshev polynomials within the KAN framework to more effectively model the complex motion characteristics inherent in IMU signals. To the best of our knowledge, this work represents the first application of an interpretable KAN model to IO. Experimental results on five publicly available datasets demonstrate the effectiveness of CKANIO.

翻译：惯性里程计（IO）完全依赖惯性测量单元（IMU）的信号进行定位，为消费级定位提供了一条前景广阔的途径。然而，对IMU信号中存在的非线性运动模式进行精确建模，仍然是限制IO精度的主要瓶颈。为应对这一挑战，我们提出了CKANIO，一个集成了基于切比雪夫的柯尔莫哥洛夫-阿诺德网络（切比雪夫KAN）的IO框架。具体而言，我们设计了一种新颖的残差架构，该架构利用KAN框架内切比雪夫多项式的非线性逼近能力，以更有效地建模IMU信号中固有的复杂运动特性。据我们所知，这是可解释的KAN模型在IO领域的首次应用。在五个公开数据集上的实验结果验证了CKANIO的有效性。