Inertial Odometry (IO) enables real-time localization using only acceleration and angular velocity measurements from an Inertial Measurement Unit (IMU), making it a promising solution for localization in consumer-grade applications. Traditionally, researchers have routinely transformed IMU measurements into the global frame to obtain smoother motion representations. However, recent studies in drone scenarios have demonstrated that the body frame can significantly improve localization accuracy, prompting a re-evaluation of the suitability of the global frame for pedestrian IO. To address this issue, this paper systematically evaluates the effectiveness of the global frame in pedestrian IO through theoretical analysis, qualitative inspection, and quantitative experiments. Building upon these findings, we further propose MambaIO, which decomposes IMU measurements into high-frequency and low-frequency components using a Laplacian pyramid. The low-frequency component is processed by a Mamba architecture to extract implicit contextual motion cues, while the high-frequency component is handled by a convolutional structure to capture fine-grained local motion details. Experiments on multiple public datasets show that MambaIO substantially reduces localization error and achieves state-of-the-art (SOTA) performance. To the best of our knowledge, this is the first application of the Mamba architecture to the IO task.

翻译：惯性里程计仅利用惯性测量单元提供的加速度和角速度测量值即可实现实时定位，这使其成为消费级应用中极具前景的定位解决方案。传统上，研究者通常将IMU测量值转换至全局坐标系以获得更平滑的运动表征。然而，近期在无人机场景中的研究表明，机体坐标系能显著提升定位精度，这促使我们重新评估全局坐标系在行人惯性里程计中的适用性。为解决此问题，本文通过理论分析、定性检验与定量实验，系统评估了全局坐标系在行人惯性里程计中的有效性。基于这些发现，我们进一步提出了MambaIO，该方法利用拉普拉斯金字塔将IMU测量值分解为高频与低频分量。低频分量由Mamba架构处理以提取隐式上下文运动线索，而高频分量则由卷积结构处理以捕捉细粒度的局部运动细节。在多个公开数据集上的实验表明，MambaIO显著降低了定位误差，并达到了最先进的性能。据我们所知，这是Mamba架构在惯性里程计任务中的首次应用。