Pedestrian inertial odometry (PIO) estimates autonomous pedestrian motion using only acceleration and angular velocity measurements collected by an inertial measurement unit (IMU), making it highly valuable for consumer level localization applications. However, under a dual device acquisition setting, IMU signals collected by a freely carried mobile device are inherently composite signals in which the global motion of the human torso is coupled with perturbations induced by local limb motion. This coupling makes accurate human motion modeling more challenging. To address this issue, this paper proposes frequency decomposed inertial odometry (FDIO). The proposed method first decomposes input IMU signals into low frequency and high frequency components using a Laplacian pyramid. It then adopts a Mamba module to model long range motion information from the low frequency component and uses a multi scale convolution module to extract fine grained local dynamic features from the high frequency component. Experiments on five public PIO datasets show that FDIO achieves an average absolute trajectory error of 3.221~m and an average relative trajectory error of 2.550~m, reducing the errors by 33.3\% and 16.7\% compared with the RoNIN ResNet baseline, respectively. These results validate the effectiveness of the proposed frequency decomposition strategy. To the best of our knowledge, this work is among the first efforts to introduce Mamba and a frequency decomposition architecture into inertial odometry.

翻译：行人惯性里程计利用惯性测量单元采集的加速度和角速度测量值估计自主行人运动，使其在消费级定位应用中具有重要价值。然而，在双设备采集场景下，由自由携带的移动设备采集的惯性测量单元信号本质上是复合信号，其中人体躯干的整体运动与局部肢体运动引起的扰动相互耦合。这种耦合使得精确的人体运动建模更具挑战性。为解决该问题，本文提出频率分解惯性里程计。该方法首先利用拉普拉斯金字塔将输入惯性测量单元信号分解为低频和高频分量，随后采用Mamba模块从低频分量中建模长程运动信息，并利用多尺度卷积模块从高频分量中提取细粒度局部动态特征。在五个公开行人惯性里程计数据集上的实验表明，FDIO实现了平均绝对轨迹误差3.221米和平均相对轨迹误差2.550米，相比RoNIN ResNet基线分别降低了33.3%和16.7%的误差。这些结果验证了所提频率分解策略的有效性。据我们所知，本研究是首次将Mamba和频率分解架构引入惯性里程计的探索之一。