A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors

TL;DR

This paper introduces a fast, robust online orientation estimation algorithm that combines inertial sensor data with a gradient descent correction, reducing computational load and improving accuracy under disturbances.

Contribution

The proposed algorithm offers a novel, computationally efficient method for real-time orientation estimation that is robust to model errors and disturbances, outperforming existing methods.

Findings

Reduces computational complexity by approximately 33%.

Demonstrates robustness against large accelerations and magnetic disturbances.

Improves accuracy of orientation estimates during significant corrections.

Abstract

We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration drift using accelerometer and magnetometer data. This correction is computed, at each time instance, using a single gradient descent step with fixed step length. This fixed step length results in robustness against model errors, e.g. caused by large accelerations or by short-term magnetic field disturbances, which we numerically illustrate using Monte Carlo simulations. Our algorithm estimates a three-dimensional update to the orientation rather than the entire orientation itself. This reduces the computational complexity by approximately 1/3 with respect to the state of the art. It also improves the quality of the resulting estimates, specifically when the orientation corrections are large. We illustrate the efficacy of the algorithm using experimental data.