End-to-End Deep Learning Framework for Real-Time Inertial Attitude
Estimation using 6DoF IMU
Abstract
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 five 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 five 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.