Inertial Odometry (IO) has gained attention in quadrotor applications due to its sole reliance on inertial measurement units (IMUs), attributed to its lightweight design, low cost, and robust performance across diverse environments. However, most existing learning-based inertial odometry systems for quadrotors either use only IMU data or include additional dynamics-related inputs such as thrust, but still lack a principled formulation of the underlying physical model to be learned. This lack of interpretability hampers the model's ability to generalize and often limits its accuracy. In this work, we approach the inertial odometry learning problem from a different perspective. Inspired by the aerodynamics model and IMU measurement model, we identify the key physical quantity--rotor speed measurements required for inertial odometry and design a transformer-based inertial odometry. By incorporating rotor speed measurements, the proposed model improves velocity prediction accuracy by 36.9%. Furthermore, the transformer architecture more effectively exploits temporal dependencies for denoising and aerodynamics modeling, yielding an additional 22.4% accuracy gain over previous results. To support evaluation, we also provide a real-world quadrotor flight dataset capturing IMU measurements and rotor speed for high-speed motion. Finally, combined with an uncertainty-aware extended Kalman filter (EKF), our framework is validated across multiple datasets and real-time systems, demonstrating superior accuracy, generalization, and real-time performance. We share the code and data to promote further research (https://github.com/SJTU-ViSYS-team/AI-IO).

翻译：惯性里程计因其仅依赖惯性测量单元，具有轻量化、低成本及在多样化环境中鲁棒性强的特点，在四旋翼无人机应用中受到关注。然而，现有大多数基于学习的四旋翼惯性里程计系统要么仅使用IMU数据，要么额外引入推力等动力学相关输入，但仍缺乏对需学习的底层物理模型的原则性建模。这种可解释性的缺失限制了模型的泛化能力，并常常制约其精度。本文从不同视角探讨惯性里程计的学习问题。受空气动力学模型与IMU测量模型的启发，我们识别出惯性里程计所需的关键物理量——旋翼转速测量值，并设计了一种基于Transformer的惯性里程计。通过引入旋翼转速测量，所提模型将速度预测精度提升了36.9%。此外，Transformer架构能更有效地利用时序依赖性进行去噪与空气动力学建模，相比先前结果额外带来22.4%的精度提升。为支持评估，我们还提供了一个包含高速运动下IMU测量值与旋翼转速的真实四旋翼飞行数据集。最后，结合不确定性感知扩展卡尔曼滤波器，我们的框架在多个数据集与实时系统中得到验证，展现出卓越的精度、泛化能力与实时性能。我们公开代码与数据以促进后续研究（https://github.com/SJTU-ViSYS-team/AI-IO）。