TinyIO通过轻量化设计实现高精度惯性定位，适合移动端部署。
TinyIO: Lightweight Reparameterized Inertial Odometry
- 多分支训练+单路径推理，降低计算开销
- 在RoNIN上相比R-ResNet降低23.53%的绝对轨迹误差
- 双路自适应注意力机制，几乎不增加参数量
惯性里程计（IO）广泛应用于移动设备定位，但同时实现轻量化与高精度仍具挑战。为此，我们提出TinyIO，一种轻量级惯性里程计方法。训练阶段采用多分支结构更有效提取运动特征；推理时将训练好的多分支模型转换为等效单路径结构以减少计算复杂度。此外，提出双路径自适应注意力机制（DPAA），在通道和时间维度上增强对上下文运动的感知，附加参数极少。在公开数据集上的大量实验表明，该方法在准确率与模型尺寸间取得良好平衡。在RoNIN数据集上，相比R-ResNet，TinyIO将绝对轨迹误差（ATE）降低23.53%，参数量减少3.68%。
原文摘要 · Abstract (English)
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%.
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