StarIO: A Lightweight Inertial Odometry for Nonlinear Motion

TL;DR

StarIO introduces a lightweight inertial odometry framework that effectively models complex nonlinear motion, significantly reducing drift errors and outperforming existing methods across multiple datasets.

Contribution

The paper presents a novel inertial odometry approach using high-dimensional nonlinear feature projection, collaborative attention, and multi-scale gated convolutions for improved nonlinear motion modeling.

Findings

Outperforms state-of-the-art methods on six inertial datasets.

Reduces ATE by 2.26% to 65.78% on the RoNIN dataset.

Establishes new benchmarks in inertial odometry accuracy.

Abstract

Inertial odometry (IO) directly estimates the position of a carrier from inertial sensor measurements and serves as a core technology for the widespread deployment of consumer grade localization systems. While existing IO methods can accurately reconstruct simple and near linear motion trajectories, they often fail to account for drift errors caused by complex motion patterns such as turning. This limitation significantly degrades localization accuracy and restricts the applicability of IO systems in real world scenarios. To address these challenges, we propose a lightweight IO framework. Specifically, inertial data is projected into a high dimensional implicit nonlinear feature space using the Star Operation method, enabling the extraction of complex motion features that are typically overlooked. We further introduce a collaborative attention mechanism that jointly models global motion dynamics across both channel and temporal dimensions. In addition, we design Multi Scale Gated Convolution Units to capture fine grained dynamic variations throughout the motion process, thereby enhancing the model's ability to learn rich and expressive motion representations. Extensive experiments demonstrate that our proposed method consistently outperforms SOTA baselines across six widely used inertial datasets. Compared to baseline models on the RoNIN dataset, it achieves reductions in ATE ranging from 2.26% to 65.78%, thereby establishing a new benchmark in the field.