Inertial odometry (IO) leverages inertial measurement unit (IMU) signals for cost-effective localization. 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.
@article{arxiv.2507.16120,
title = {FTIN: Frequency-Time Integration Network for Inertial Odometry},
author = {Shanshan Zhang and Qi Zhang and Siyue Wang and Liqin Wu and Tianshui Wen and Ziheng Zhou and Ao Peng and Xuemin Hong and Lingxiang Zheng and Yu Yang},
journal= {arXiv preprint arXiv:2507.16120},
year = {2025}
}