With the rapid growth of bike sharing and the increasing diversity of cycling applications, accurate bicycle localization has become essential. traditional GNSS-based methods suffer from multipath effects, while existing inertial navigation approaches rely on precise modeling and show limited robustness. Tight Learned Inertial Odometry (TLIO) achieves low position drift by combining raw IMU data with predicted displacements by neural networks, but its high computational cost restricts deployment on mobile devices. To overcome this, we extend TLIO to bicycle localization and introduce an improved Mixture-of Experts (MoE) model that reduces both training and inference costs. Experiments show that, compared to the state-of-the-art LLIO framework, our method achieves comparable accuracy while reducing parameters by 64.7% and computational cost by 81.8%.

翻译：随着共享单车的快速发展和骑行应用场景的日益多样化，精确的自行车定位变得至关重要。传统的基于全球导航卫星系统（GNSS）的方法易受多路径效应影响，而现有的惯性导航方法依赖于精确的建模且鲁棒性有限。紧耦合学习型惯性里程计（TLIO）通过将原始惯性测量单元（IMU）数据与神经网络预测的位移相结合，实现了较低的位置漂移，但其较高的计算成本限制了在移动设备上的部署。为克服这一限制，我们将TLIO扩展至自行车定位领域，并引入一种改进的专家混合（MoE）模型，该模型同时降低了训练和推理成本。实验表明，与最先进的LLIO框架相比，我们的方法在达到相当精度的同时，参数量减少了64.7%，计算成本降低了81.8%。