MambaIO: Global-Coordinate Inertial Odometry for Pedestrians via Multi-Scale Frequency-Decoupled Modeling

TL;DR

MambaIO introduces a multi-scale frequency-decoupled approach for pedestrian inertial odometry, leveraging body frame measurements and a novel Mamba architecture to improve localization accuracy over traditional global frame methods.

Contribution

The paper proposes MambaIO, a novel frequency-decoupled inertial odometry method using the Mamba architecture, achieving state-of-the-art pedestrian localization accuracy.

Findings

MambaIO significantly reduces localization error.

It outperforms existing methods on multiple datasets.

First application of Mamba architecture to IO.

Abstract

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.