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%.

翻译：惯性里程计（IO）是一种在移动设备上广泛使用的定位方法；然而，获取一个同时实现高精度的轻量级IO模型仍然具有挑战性。为了解决这个问题，我们提出了TinyIO，一种轻量级的IO方法。在训练期间，我们采用多分支架构以更有效地提取多样化的运动特征。在推理时，训练好的多分支模型被转换为等效的单路径架构以降低计算复杂度。我们进一步提出了一种双路径自适应注意力机制（DPAA），该机制以可忽略的额外参数增强了TinyIO在通道和时间维度上对上下文运动的感知能力。在公开数据集上的大量实验表明，我们的方法在精度和模型大小之间取得了良好的平衡。在RoNIN数据集上，与R-ResNet相比，TinyIO将ATE降低了23.53%，并将参数量减少了3.68%。