# A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors

arXiv: https://arxiv.org/abs/1910.00463 · HTML: https://mlsanity.com/paper/1910.00463/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods](https://mlsanity.com/paper/2304.06548/) (2023)

## Abstract (arXiv)

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.