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In this note, the attitude and inertial sensors drift biases estimation for Strapdown inertial navigation system is investigated. A semi-analytic method is proposed, which contains two interlaced solution procedures. Specifically, the attitude encoding the body frame changes and gyroscopes drift biases are estimated through attitude estimation while the attitude encoding the constant value at the very start and accelerometers drift biases are determined through online optimization.
This paper proposes a dynamic analytical initialization method for spacecraft attitude estimators. In the proposed method, the desired attitude matrix is decomposed into two parts: one is the constant attitude matrix at the very start and the other e
Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them still suffer from 1) inaccurate initial estimation, 2) inaccurate ini
This paper proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data, and estimating in real time the orientation (attitude) of a robot in dead reckoning. The obtained algorithm outperforms the s
This paper presents a novel filter with low computational demand to address the problem of orientation estimation of a robotic platform. This is conventionally addressed by extended Kalman filtering of measurements from a sensor suit which mainly inc
In this note, we have revisited the previously published paper Particle Filtering for Attitude Estimation Using a Minimal Local-Error Representation. In the revisit, we point out that the quaternion particle filtering based on the local/global repres