AI-IO：一种受空气动力学启发的实时惯性里程计用于四旋翼
AI-IO: An Aerodynamics-Inspired Real-Time Inertial Odometry for Quadrotors
AI总结：
本文提出了一种基于变压器的AI-IO方法，通过引入旋翼速度测量提升四旋翼惯性里程计的精度和实时性能。
摘要：
惯性里程计（IO）在四旋翼应用中受到关注，因其仅依赖于惯性测量单元（IMUs），归因于其轻巧的设计、低成本以及在多样化环境中的鲁棒性能。然而，现有的基于学习的四旋翼惯性里程计系统要么仅使用IMU数据，要么包括额外的动力学相关输入，如推力，但仍然缺乏对底层物理模型的系统性描述以进行学习。这种缺乏可解释性阻碍了模型的泛化能力，通常限制了其精度。在本工作中，我们从不同的角度来处理惯性里程计学习问题。受空气动力学模型和IMU测量模型的启发，我们识别了关键的物理量——用于惯性里程计的旋翼速度测量，并设计了基于变压器的惯性里程计。通过引入旋翼速度测量，所提出的模型将速度预测精度提高了36.9%。此外，变压器架构更有效地利用时间依赖性以进行去噪和空气动力学建模，从而在先前结果上又提高了22.4%的精度。为了支持评估，我们还提供了一个真实世界四旋翼飞行数据集，捕捉了IMU测量和旋翼速度以记录高速运动。最后，结合一种不确定性感知的扩展卡尔曼滤波器（EKF），我们的框架在多个数据集和实时系统中得到验证，展示了优越的精度、泛化能力和实时性能。我们分享了代码和数据以促进进一步研究（https://github.com/SJTU-ViSYS-team/AI-IO）.
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
