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This work describes a family of attitude estimators that are based on a generalization of Mahonys nonlinear complementary filter. This generalization reveals the close mathematical relationship between the nonlinear complementary filter and the more traditional multiplicative extended Kalman filter. In fact, the bias-free and constant gain multiplicative continuous-time extended Kalman filters may be interpreted as special cases of the generalized attitude estimator. The correspondence provides a rational means of choosing the gains for the nonlinear complementary filter and a proof of the near global asymptotic stability of special cases of the multiplicative extended Kalman filter.
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
Nonlinear acceleration algorithms improve the performance of iterative methods, such as gradient descent, using the information contained in past iterates. However, their efficiency is still not entirely understood even in the quadratic case. In this
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
The most important ingredient for solving mixed-integer nonlinear programs (MINLPs) to global epsilon-optimality with spatial branch and bound is a tight, computationally tractable relaxation. Due to both theoretical and practical considerations, rel
In this paper, the spacecraft attitude estimation problem has been investigated making use of the concept of matrix Lie group. Through formulation of the attitude and gyroscope bias as elements of SE(3), the corresponding extended Kalman filter, term