CKANIO: Learnable Chebyshev Polynomials for Inertial Odometry

TL;DR

CKANIO introduces a novel Chebyshev polynomial-based neural network architecture to improve inertial odometry accuracy by better modeling complex IMU signal motions, demonstrating superior results on multiple datasets.

Contribution

This work is the first to apply an interpretable Kolmogorov-Arnold Network with Chebyshev polynomials to inertial odometry for enhanced nonlinear motion modeling.

Findings

Outperforms existing methods on five datasets

Effectively models complex IMU signals

Demonstrates improved localization accuracy

Abstract

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.