用频域+时域融合提升惯性里程计的精度与效率
FTIN: Frequency-Time Integration Network for Inertial Odometry
- 通过频域与时域联合建模，捕捉全局运动模式
- 在多个公开数据集上显著降低定位误差
- 适合需要高精度实时定位的机器人应用
惯性里程计（IO）利用惯性测量单元（IMU）信号实现低成本定位。然而，高采样率带来的大量冗余数据会阻碍IO对关键信息的关注，形成信息瓶颈。为此，本文提出一种跨域融合的IO框架，结合频域与时域信息。具体而言，利用频域表示的全局上下文与能量集中特性，捕捉整体运动模式并缓解瓶颈问题。据我们所知，这是首次将频域特征处理引入惯性里程计。在多个公开数据集上的实验结果证明了该频-时域融合策略的有效性。
原文摘要 · Abstract (English)
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.
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