  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.
