Inertial odometry (IO) using strap-down inertial measurement units (IMUs) is critical in many robotic applications where precise orientation and position tracking are essential. Prior kinematic motion model-based IO methods often use a simplified linearized IMU noise model and thus usually encounter difficulties in modeling non-deterministic errors arising from environmental disturbances and mechanical defects. In contrast, data-driven IO methods struggle to accurately model the sensor motions, often leading to generalizability and interoperability issues. To address these challenges, we present AirIMU, a hybrid approach to estimate the uncertainty, especially the non-deterministic errors, by data-driven methods and increase the generalization abilities using model-based methods. We demonstrate the adaptability of AirIMU using a full spectrum of IMUs, from low-cost automotive grades to high-end navigation grades. We also validate its effectiveness on various platforms, including hand-held devices, vehicles, and a helicopter that covers a trajectory of 262 kilometers. In the ablation study, we validate the effectiveness of our learned uncertainty in an IMU-GPS pose graph optimization experiment, achieving a 31.6\% improvement in accuracy. Experiments demonstrate that jointly training the IMU noise correction and uncertainty estimation synergistically benefits both tasks.

翻译：惯性里程计（IO）利用捷联惯性测量单元（IMU）在许多需要精确姿态和位置跟踪的机器人应用中至关重要。先前的基于运动学运动模型的IO方法通常采用简化线性化的IMU噪声模型，因此在建模由环境扰动和机械缺陷引起的非确定性误差时常常遇到困难。相比之下，数据驱动的IO方法难以准确建模传感器运动，通常导致泛化性和互操作性问题。为应对这些挑战，我们提出AirIMU，一种混合方法，通过数据驱动方法估计不确定性（尤其是非确定性误差），并利用基于模型的方法增强泛化能力。我们使用从低成本汽车级到高端导航级的全谱IMU展示了AirIMU的适应性。我们还验证了其在各种平台上的有效性，包括手持设备、车辆以及覆盖262公里轨道的直升机。在消融研究中，我们在IMU-GPS位姿图优化实验中验证了所学不确定性的有效性，实现了31.6%的精度提升。实验表明，联合训练IMU噪声校正和不确定性估计能够协同提升两项任务的性能。