通过保留机体坐标系提升惯性里程计精度，适用于复杂飞行的无人机。
AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability
- 保留体坐标系下IMU数据，增强运动可观测性。
- 跨三个数据集平均精度提升66.7%，额外提升23.8%。
- 无需外部传感器，适合真实飞行场景的无人机部署。
仅使用惯性测量单元（IMU）的惯性里程计（IO）为无人机（UAV）提供了轻量且低成本的解决方案，但现有基于学习的IO模型在面对无人机高度动态、非线性的飞行模式时泛化能力差，与行人运动差异显著。本文指出，将原始IMU数据转换到全局坐标系会削弱无人机关键运动信息的可观测性。通过保持体坐标系表示，本方法在三个数据集上平均精度提升66.7%。进一步地，显式编码姿态信息至运动网络，相较之前结果再提升23.8%。结合数据驱动的IMU校正模型（AirIMU）和不确定性感知的扩展卡尔曼滤波器（EKF），该方法在激进飞行中仍能实现鲁棒状态估计，无需依赖外部传感器或控制输入。值得注意的是，该方法对未见训练数据也表现出强泛化能力，展现出在真实无人机应用中的潜力。
原文摘要 · Abstract (English)
Inertial odometry (IO) using only Inertial Measurement Units (IMUs) offers a lightweight and cost-effective solution for Unmanned Aerial Vehicle (UAV) applications, yet existing learning-based IO models often fail to generalize to UAVs due to the highly dynamic and non-linear-flight patterns that differ from pedestrian motion. In this work, we identify that the conventional practice of transforming raw IMU data to global coordinates undermines the observability of critical kinematic information in UAVs. By preserving the body-frame representation, our method achieves substantial performance improvements, with a 66.7% average increase in accuracy across three datasets. Furthermore, explicitly encoding attitude information into the motion network results in an additional 23.8% improvement over prior results. Combined with a data-driven IMU correction model (AirIMU) and an uncertainty-aware Extended Kalman Filter (EKF), our approach ensures robust state estimation under aggressive UAV maneuvers without relying on external sensors or control inputs. Notably, our method also demonstrates strong generalizability to unseen data not included in the training set, underscoring its potential for real-world UAV applications.
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