Abstract
This paper presents a novel method for attitude estimation of an object in 3D space by incremental learning of the Long-Short Term Memory (LSTM) network. Gyroscope, accelerometer, and magnetometer are few widely used sensors in attitude estimation applications. Traditionally, multi-sensor fusion methods such as the Extended Kalman Filter and Complementary Filter are employed to fuse the measurements from these sensors. However, these methods exhibit limitations in accounting for the uncertainty, unpredictability, and dynamic nature of the motion in real-world situations. In this paper, the inertial sensors data are fed to the LSTM network which are then updated incrementally to incorporate the dynamic changes in motion occurring in the run time. The robustness and efficiency of the proposed framework is demonstrated on the dataset collected from a commercially available inertial measurement unit. The proposed framework offers a significant improvement in the results compared to the traditional method, even in the case of a highly dynamic environment. The LSTM framework-based attitude estimation approach can be deployed on a standard AI-supported processing module for real-time applications.
Highlights
Sensor fusion is the process of combining information from multiple sensors to provide an improved version of the output compared to that from an individual sensor (White, 1991)
The main contributions of this paper are two fold; firstly, we have demonstrated the use of the Long-Short Term Memory (LSTM) network for attitude estimation and compared its performance with the traditional approach, Extended Kalman Filter (EKF)
The proposed deep learning-based LSTM neural network structure for attitude estimation is applied to the different datasets collected from the commercially available AHRS module
Summary
Sensor fusion is the process of combining information from multiple sensors to provide an improved version of the output compared to that from an individual sensor (White, 1991). It provides the advantage of improved accuracy, increased precision and robustness. One of the relevant and important sensor fusion applications is attitude estimation, which plays a vital role in detecting the position and orientation of the moving body in 3D space. The object’s orientation in the 3-dimensional space is termed as the attitude of the object (Titterton, Weston & Weston, 2004) and is represented as the rotation about the three orthogonal axes as Roll, Pitch, and Yaw
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
