Incremental learning of LSTM framework for sensor fusion in attitude estimation

TL;DR

This paper introduces an incremental learning LSTM framework for real-time attitude estimation using inertial sensors, outperforming traditional methods in dynamic environments.

Contribution

It proposes a novel incremental learning LSTM approach for sensor fusion in attitude estimation, enhancing robustness and adaptability in real-time scenarios.

Findings

Significant improvement over traditional methods in dynamic environments

Robustness demonstrated on real inertial measurement data

Suitable for deployment on AI-supported processing modules

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.