TinyIO: Lightweight Reparameterized Inertial Odometry

TL;DR

TinyIO is a lightweight inertial odometry method that uses multi-branch training and a dual-path attention mechanism to achieve high accuracy with fewer parameters, suitable for mobile localization.

Contribution

The paper introduces TinyIO, a novel lightweight IO model with a multi-branch training architecture and a dual-path adaptive attention mechanism for improved efficiency and accuracy.

Findings

Reduces ATE by 23.53% on RoNIN dataset

Decreases model parameters by 3.68%

Achieves favorable accuracy-size trade-off

Abstract

Inertial odometry (IO) is a widely used approach for localization on mobile devices; however, obtaining a lightweight IO model that also achieves high accuracy remains challenging. To address this issue, we propose TinyIO, a lightweight IO method. During training, we adopt a multi-branch architecture to extract diverse motion features more effectively. At inference time, the trained multi-branch model is converted into an equivalent single-path architecture to reduce computational complexity. We further propose a Dual-Path Adaptive Attention mechanism (DPAA), which enhances TinyIO's perception of contextual motion along both channel and temporal dimensions with negligible additional parameters. Extensive experiments on public datasets demonstrate that our method attains a favorable trade-off between accuracy and model size. On the RoNIN dataset, TinyIO reduces the ATE by 23.53% compared with R-ResNet and decreases the parameter count by 3.68%.