Inertial Measurement Units (IMU) are commonly used in inertial attitude
estimation from engineering to medical sciences. There may be disturbances and
high dynamics in the environment of these applications. Also, their motion
characteristics and patterns also may differ. Many conventional filters have
been proposed to tackle the inertial attitude estimation problem based on IMU
measurements. There is no generalization over motion and environmental
characteristics in these filters. As a result, the presented conventional
filters will face various motion characteristics and patterns, which will limit
filter performance and need to optimize the filter parameters for each
situation. In this paper, two end-to-end deep-learning models are proposed to
solve the problem of real-time attitude estimation by using inertial sensor
measurements, which are generalized to motion patterns, sampling rates, and
environmental disturbances. The proposed models incorporate accelerometer and
gyroscope readings as inputs, which are collected from a combination of seven
public datasets. The models consist of convolutional neural network (CNN)
layers combined with Bi-Directional Long-Short Term Memory (LSTM) followed by a
Fully Forward Neural Network (FFNN) to estimate the quaternion. To evaluate the
validity and reliability, we have performed an extensive and comprehensive
evaluation over seven publicly available datasets, which consist of more than
120 hours and 200 kilometers of IMU measurements. The results show that the
proposed method outperforms the state-of-the-art methods in terms of accuracy
and robustness. Furthermore, it demonstrates that this model generalizes better
than other methods over various motion characteristics and sensor sampling
rates.

本文提出两种端到端的深度学习模型，用于实时姿态估计，基于惯性传感器测量，普适于运动模式、采集率和环境干扰的多样化情况。所提出的模型将加速计和陀螺仪读数作为输入，采集自七个公共数据集的组合。模型由卷积神经网络 (CNN) 层、双向长短时记忆 (LSTM) 和全向前神经网络 (FFNN) 组成，用于估计四元数，并在超过 120 个小时和 200 公里的 IMU 测量数据上进行了广泛和全面的评估。研究结果表明，所提出的方法在精度和稳健性方面优于最先进的方法，并且它演示了与其他方法相比，在各种运动特性和传感器采样率方面，该模型具有更好的泛化性。