StarIO: A Lightweight Inertial Odometry for Nonlinear Motion

Abstract

Inertial odometry (IO) is an attractive approach for consumer-grade localization. However, existing data-driven IO methods often suffer from significant drift under complex nonlinear motion patterns (e.g., turns), as they struggle to capture the nonlinear relationships between Inertial Measurement Unit (IMU) signals and motion states. To address this issue, we propose a lightweight IO model, StarIO. Specifically, we first apply the Star Operation to project IMU signals into a high-dimensional implicit nonlinear feature space, enabling effective extraction of the complex nonlinear motion characteristics that typically cause drift. We then capture contextual dependencies across both the temporal and channel dimensions to enhance trajectory estimation over long sequences.In addition, we introduce a multi-scale gated unit that fuses fine-grained local motion dynamics with contextual information to achieve a comprehensive representation of motion. Extensive experiments on six representative open-source datasets demonstrate that StarIO achieves a superior trade-off between model lightweightness and localization accuracy.For example, on the RoNIN dataset, our approach reduces the ATE by 5.21% compared to R-ResNet while using only 2.762M parameters.