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.

翻译：本文提出一种新颖的端到端深度学习框架，用于基于6自由度（6DoF）惯性测量单元（IMU）数据的实时惯性姿态估计。惯性测量单元广泛应用于工程和医学科学等多个领域。然而，传统姿态估计滤波器在不同运动模式和环境干扰下泛化能力较差。为解决这一问题，我们提出两种深度学习模型，将加速度计和陀螺仪读数作为输入。这些模型设计为能够泛化至不同运动模式、采样率和环境干扰。我们的模型由卷积神经网络层结合双向长短期记忆网络，后接全前馈神经网络组成，用于估计四元数。我们在七个公开数据集上评估所提方法，累计超过120小时和200公里的IMU测量数据。结果表明，所提方法在精度和鲁棒性方面均优于现有最优方法。此外，我们的框架在多种运动特征和传感器采样率下展现出卓越的泛化性能。总体而言，本文为基于6DoF IMU的实时惯性姿态估计提供了一种全面可靠的解决方案，对广泛的应用领域具有重要意义。