Orientation Estimation Piloted by Deep Reinforcement Learning
Miaomiao Liu, Sikai Yang, Arya Rathee, Wan Du · 2024
Inertial Measurement Unit (IMU) sensors are commonly used for estimating device orientation. However, due to the irregular movements of devices and distortions of magnetic fields, IMU sensors may present varying data quality. Conventional data fusion approaches such as Complementary Filter (CF) and Kalman Filter struggle to adapt to these variations. Recent efforts have explored the utilization of deep learning to directly infer orientation from IMU sensor data. Nevertheless, when facing new scenarios that have different data distributions from training (e.g., different movement patterns or magnetic fields), deep learning methods cannot accurately infer orientation. In this paper, we conduct extensive experiments and identify two critical parameters for CF-based orientation estimation. We propose employing deep learning to adjust these two parameters, rather than directly inferring the final orientation outcomes. Since the relationship between sensor data and the settings of CF parameters is relatively simpler than the relationship between sensor data and orientation, a deep learning model of the same size can learn the first relationship more effectively and efficiently. We develop DRLPilot which leverages Deep Reinforcement Learning (DRL) to pilot CF-based orientation estimation based on the data quality of IMU sensors. Our DRL framework incorporates novel state design and reward function to accommodate the unique features of IMU sensor data and orientation estimation. Extensive experiment results demonstrate DRLPilot outperforms baseline systems by 27% in orientation accuracy.