AirIMU: Learning Uncertainty Propagation for Inertial Odometry

• URL: http://arxiv.org/abs/2310.04874v4
• Date: Wed, 15 May 2024 16:14:00 GMT
• Title: AirIMU: Learning Uncertainty Propagation for Inertial Odometry
• Authors: Yuheng Qiu, Chen Wang, Can Xu, Yutian Chen, Xunfei Zhou, Youjie Xia, Sebastian Scherer,
• Abstract summary: Inertial odometry (IO) using strap-down inertial measurement units (IMUs) is critical in many robotic applications. We present AirIMU, a hybrid approach to estimate the uncertainty, especially the non-deterministic errors, by data-driven methods. We demonstrate its effectiveness on various platforms, including hand-held devices, vehicles, and a helicopter that covers a trajectory of 262 kilometers.
• Score: 29.093168179953185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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