FTIN: Frequency-Time Integration Network for Inertial Odometry

However, high IMU sampling rates introduce substantial redundancy that impedes IO's ability to attend to salient components, thereby creating an information bottleneck. To address this challenge, we propose a cross-domain IO framework that fuses information from the frequency and time domains. Specifically, we exploit the global context and energy-compaction properties of frequency-domain representations to capture holistic motion patterns and alleviate the bottleneck. To the best of our knowledge, this is among the first attempts to incorporate frequency-domain feature processing into IO. Experimental results on multiple public datasets demonstrate the effectiveness of the proposed frequency-time-domain fusion strategy. Index T erms-- Frequency-Domain Learning, Inertial Odometry, Inertial Measurement Unit signals 1. INTRODUCTION Inertial odometry (IO) aims to reconstruct motion trajectories from high-frequency inertial measurement unit (IMU) signals--comprising tri-axial accelerometer and gyroscope data--in order to enable low-cost and robust localization [1, 2].