TinyIO: Lightweight Reparameterized Inertial Odometry
This work addresses the problem of efficient localization on mobile devices, representing an incremental improvement in model design.
The paper tackles the challenge of creating a lightweight inertial odometry model for mobile devices by proposing TinyIO, which reduces the absolute trajectory error by 23.53% and parameters by 3.68% compared to a baseline on the RoNIN dataset.
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%.