Portrait of James Richard Forbes

James Richard Forbes

Associate Academic Member

james.richard.forbes@mcgill.ca

Full Professor, McGill University, Department of Mechanical Engineering

Research Topics

Applied Machine Learning

Autonomous Robotics Navigation

Information Theory

Lie Group Theory

Optimization

Robotics

Current Students

Timothy Everett Adams

Master's Research - McGill University

timothy.adams@mila.quebec

Master's Research - McGill University

angad.bajwa@mila.quebec

Master's Research - McGill University

pierre.chamoun@mila.quebec

PhD - McGill University

mitchell.cohen@mila.quebec

Nicholas Dahdah

Master's Research - McGill University

nicholas.dahdah@mila.quebec

Arturo Del Castillo Bernal

PhD - McGill University

arturo.del-castillo@mila.quebec

PhD - McGill University

jonathan.eid@mila.quebec

Master's Research - McGill University

mohammad.ghali@mila.quebec

PhD - McGill University

kyungmin.jung@mila.quebec

Vassili Korotkine

PhD - McGill University

vassili.korotkine@mila.quebec

Isabel Lougheed

Master's Research - McGill University

isabel.lougheed@mila.quebec

Undergraduate - McGill University

ireneasimier@gmail.com

Andrew Stirling

Master's Research - McGill University

andrew.stirling2@mail.mcgill.ca

Master's Research - McGill University

Principal supervisor :

Derek Nowrouzezahrai

junha.yoo@mila.quebec

PhD - McGill University

yanting.zhou@mila.quebec

Publications

The Invariant Rauch-Tung-Striebel Smoother

Niels van der Laan

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 … (see more)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 (published)