{"ID":2891564,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16120","arxiv_id":"2507.16120","title":"FTIN: Frequency-Time Integration Network for Inertial Odometry","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.16120","url_pdf":"https://arxiv.org/pdf/2507.16120v2","authors":"[\"Shanshan Zhang\",\"Qi Zhang\",\"Siyue Wang\",\"Liqin Wu\",\"Tianshui Wen\",\"Ziheng Zhou\",\"Ao Peng\",\"Xuemin Hong\",\"Lingxiang Zheng\",\"Yu Yang\"]","published":"2025-07-22T00:18:54Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}