Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6DoF Inertial Measurement Units
It provides a generalizable solution for real-time attitude estimation in applications like engineering and medical sciences, though it appears incremental as it builds on existing deep learning techniques.
This paper tackled the problem of poor generalization in traditional inertial attitude estimation filters by proposing a novel end-to-end deep learning framework using 6DoF IMU measurements, achieving superior accuracy and robustness over state-of-the-art methods across seven datasets totaling over 120 hours and 200 kilometers of data.
This paper presents a novel end-to-end deep learning framework for real-time inertial attitude estimation using 6DoF IMU measurements. Inertial Measurement Units are widely used in various applications, including engineering and medical sciences. However, traditional filters used for attitude estimation suffer from poor generalization over different motion patterns and environmental disturbances. To address this problem, we propose two deep learning models that incorporate accelerometer and gyroscope readings as inputs. These models are designed to be generalized to different motion patterns, sampling rates, and environmental disturbances. Our models consist of convolutional neural network layers combined with Bi-Directional Long-Short Term Memory followed by a Fully Forward Neural Network to estimate the quaternion. We evaluate the proposed method on seven publicly available datasets, totaling more than 120 hours and 200 kilometers of IMU measurements. Our results show that the proposed method outperforms state-of-the-art methods in terms of accuracy and robustness. Additionally, our framework demonstrates superior generalization over various motion characteristics and sensor sampling rates. Overall, this paper provides a comprehensive and reliable solution for real-time inertial attitude estimation using 6DoF IMUs, which has significant implications for a wide range of applications.