Incremental learning of LSTM framework for sensor fusion in attitude estimation

08/04/2021
by   Parag Narkhede, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 15

02/24/2021

R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping

In this letter, we propose a robust, real-time tightly-coupled multi-sen...
01/15/2020

Direct Visual-Inertial Ego-Motion Estimation via Iterated Extended Kalman Filter

This letter proposes a reactive navigation strategy for recovering the a...
11/25/2019

Stability of the Decoupled Extended Kalman Filter in the LSTM-Based Online Learning

We investigate the convergence and stability properties of the decoupled...
08/02/2021

Aerial Vehicles Tracking Using Noncoherent Crowdsourced Wireless Networks

Air traffic management (ATM) of manned and unmanned aerial vehicles (AVs...
10/22/2019

An Efficient EKF Based Algorithm For LSTM-Based Online Learning

We investigate online nonlinear regression with long short term memory (...
05/14/2020

Neural Networks Versus Conventional Filters for Inertial-Sensor-based Attitude Estimation

Inertial measurement units are commonly used to estimate the attitude of...
03/04/2020

Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor

Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for r...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.