用可解释的多项式网络提升惯性里程计精度
CKANIO: Learnable Chebyshev Polynomials for Inertial Odometry
- 引入切比雪夫多项式构建可解释的神经网络框架
- 在五个公开数据集上实现更高定位精度
- 适合需要高精度且可解释的惯性导航场景
惯性里程计（IO）仅依赖惯性测量单元（IMU）信号进行定位，是消费级定位的潜在解决方案。然而，准确建模IMU信号中的非线性运动模式仍是限制IO精度的主要瓶颈。为此，我们提出CKANIO，一种融合切比雪夫型柯尔莫戈罗夫-阿诺德网络（Chebyshev KAN）的IO框架。具体地，设计了一种新型残差结构，在KAN框架中利用切比雪夫多项式的非线性逼近能力，更有效地建模IMU信号中固有的复杂运动特征。据我们所知，这是首个将可解释的KAN模型应用于IO的工作。在五个公开数据集上的实验结果验证了CKANIO的有效性。
原文摘要 · Abstract (English)
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.
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