AirIO：通过增强IMU特征可观测性学习惯性里程计
AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability
AI总结：
针对现有基于学习的惯性里程计难以泛化到无人机高动态飞行场景的问题，提出AirIO方法，通过保留IMU机体坐标系表示、显式编码姿态信息，结合AirIMU校正模型与不确定性感知EKF，显著提升了无人机状态估计精度与泛化性。
摘要：
仅使用惯性测量单元（IMU）的惯性里程计（IO）为无人机（UAV）应用提供了一种轻量化且高性价比的解决方案，但现有的基于学习的IO模型通常难以泛化到无人机场景，因为无人机的高动态非线性飞行模式与行人运动存在差异。在本研究中，我们发现将原始IMU数据转换到全局坐标系的常规做法会削弱无人机关键运动学信息的可观测性。通过保留机体坐标系表示，我们的方法实现了显著的性能提升，在三个数据集上的准确率平均提升66.7%。此外，将姿态信息显式编码到运动网络中，相比之前的结果又带来了23.8%的额外提升。结合数据驱动的IMU校正模型AirIMU和具备不确定性感知的扩展卡尔曼滤波（EKF），我们的方法无需依赖外部传感器或控制输入，即可在无人机激进机动下实现鲁棒的状态估计。值得注意的是，我们的方法还对训练集之外的未见过数据展现出强泛化能力，凸显了其在实际无人机应用中的潜力。
英文摘要：
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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