A Learning-based approach for bias elimination in low cost gyroscopes

TL;DR

This paper introduces a learning-based method using a convolutional neural network to efficiently eliminate bias in low-cost gyroscopes, reducing calibration time compared to traditional analytic techniques.

Contribution

It presents a novel data-driven approach that replaces traditional calibration constraints with a neural network regression for faster bias elimination.

Findings

Bias can be effectively eliminated in shorter time using the proposed CNN-based method.

The approach outperforms traditional methods in terms of calibration speed.

Background noise is efficiently filtered from the bias signal.

Abstract

Modern sensors play a pivotal role in many operating platforms, as they manage to track the platform dynamics at a relatively low manufacturing costs. Their widespread use can be found starting from autonomous vehicles, through tactical platforms, and ending with household appliances in daily use. Upon leaving the factory, the calibrated sensor starts accumulating different error sources which slowly wear out its precision and reliability. To that end, periodic calibration is needed, to restore intrinsic parameters and realign its readings with the ground truth. While extensive analytic methods exist in the literature, little is proposed using data-driven techniques and their unprecedented approximation capabilities. In this study, we show how bias elimination in low-cost gyroscopes can be performed in considerably shorter operative time, using a unique convolutional neural network structure. The strict constraints of traditional methods are replaced by a learning-based regression which spares the time-consuming averaging time, exhibiting efficient sifting of background noise from the actual bias.