A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors
This work addresses the need for fast and robust orientation estimation in applications like robotics or motion tracking, though it appears incremental as it builds on existing sensor fusion techniques.
The paper tackles the problem of real-time orientation estimation from inertial sensors by proposing a novel algorithm that uses a single gradient descent step with fixed step length for drift correction, achieving a computational complexity reduction of approximately 1/3 compared to state-of-the-art methods and improving estimate quality for large corrections.
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.