A linearization method based on Lie algebra for pose estimation in a time horizon
Résumé
In this paper, a strategy for linear pose estimation over a time horizon is presented. This linearization is crucial for the computationally efficient formulation of predictive and optimization-based control problems in robotics. The proposed approach is based on a truncation of the Magnus expansion for the approximation of the exponential map derivative and employs Lie algebra to represent position and orientation, allowing for a unified vectorial representation in vector form that can be integrated linearly over time, offering a convenient formulation for optimization solvers. The method shows promising results for precision and computation times.
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ARK2022___A_linearization_method_based_on_Lie_algebra_for_pose_estimation_in_a_time_horizon.pdf (1.35 Mo)
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