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Equivariant Filter (EqF)
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AI
DOI：10.1109/TAC.2022.3194094.png)
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Abstract
En 中文
The kinematics of many systems encountered in robotics, mechatronics, and avionics are naturally posed on homogeneous spaces; i.e., their state lies in a smooth manifold equipped with a transitive Lie group symmetry. This article proposes a novel filter, the equivariant filter (EqF), by posing the observer state on the symmetry group, linearizing global error dynamics derived from the equivariance of the system, and applying EKF design principles. We show that equivariance of the system output can be exploited to reduce linearization error and improve filter performance. Simulation experiments of an example application show that the EqF significantly outperforms the EKF and that the reduced linearization error leads to a clear improvement in performance.
Keywords:
Observers
Estimation
Kalman filters
Robots
Design methodology
Robot kinematics
Manifolds
Algebra
control theory
kinematics
measurement
observers
robots
sensors
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
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Cited Papers
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