FTIN: Frequency-Time Integration Network for Inertial Odometry
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917017756368896 |
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| author | Zhang, Shanshan Zhang, Qi Wang, Siyue Wu, Liqin Wen, Tianshui Zhou, Ziheng Peng, Ao Hong, Xuemin Zheng, Lingxiang Yang, Yu |
| author_facet | Zhang, Shanshan Zhang, Qi Wang, Siyue Wu, Liqin Wen, Tianshui Zhou, Ziheng Peng, Ao Hong, Xuemin Zheng, Lingxiang Yang, Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FTIN: Frequency-Time Integration Network for Inertial Odometry Zhang, Shanshan Zhang, Qi Wang, Siyue Wu, Liqin Wen, Tianshui Zhou, Ziheng Peng, Ao Hong, Xuemin Zheng, Lingxiang Yang, Yu Robotics 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. |
| title | FTIN: Frequency-Time Integration Network for Inertial Odometry |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.16120 |