Portrait de James Richard Forbes

James Richard Forbes

Membre académique associé
james.richard.forbes@mcgill.ca
Professeur titulaire, McGill University, Département de génie mécanique
Sujets de recherche
Apprentissage automatique appliqué
Navigation robotique autonome
Optimisation
Robotique
Théorie de l'information
Théorie des groupes de Lie
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Étudiants actuels

Publications

The Invariant Rauch-Tung-Striebel Smoother
Niels van der Laan
Mitchell Cohen
Jonathan Arsenault
James Richard Forbes
This paper presents an invariant Rauch-Tung- Striebel (IRTS) smoother applicable to systems with states that are an element of a matrix Lie … (voir plus)group. In particular, the extended Rauch-Tung-Striebel (RTS) smoother is adapted to work within a matrix Lie group framework. The main advantage of the invariant RTS (IRTS) smoother is that the linearization of the process and measurement models is independent of the state estimate resulting in state-estimate-independent Jacobians when certain technical requirements are met. A sample problem is considered that involves estimation of the three dimensional pose of a rigid body on SE(3), along with sensor biases. The multiplicative RTS (MRTS) smoother is also reviewed and is used as a direct comparison to the proposed IRTS smoother using experimental data. Both smoothing methods are also compared to invariant and multiplicative versions of the Gauss-Newton approach to solving the batch state estimation problem.
2020-09-30
IEEE Robotics and Automation Letters (publié)
doi.org
arxiv.org