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Mitchell Cohen

mitchell.cohen@mila.quebec
PhD - McGill University
Supervisor
James Richard Forbes
Research Topics
Autonomous Robotics Navigation
Website
Google Scholar

Publications

Observability and Consistency Analysis for Visual-Inertial Navigation with Anchored Feature Parameterizations
Mitchell Cohen
Vassili Korotkine
James Richard Forbes
This paper presents an analysis of the observability and consistency properties of filtering-based visual-inertial navigation systems (VINS)… (see more) that utilize anchored feature representations. The unobservable subspace of VINS with anchored landmark parameterizations is shown to be independent of the estimated landmark state, which leads to improved estimator consistency properties without any additional modifications. However, the unobservable subspace is still found to depend on the estimated navigation state, necessitating additional consistency-enforcing techniques. Two methods to improve the consistency of VINS with anchored feature representations are presented. Simulation results showcase that all estimators employing anchored feature paramterizations exhibit improved consistency properties compared to algorithms that estimate features resolved in a global reference frame, especially in scenarios where feature initialization may be poor. Real-world experiments on the TUM-VI dataset showcase that the use of anchored feature representations alone can yield comparable performance to consistency-improved estimators employing a global feature representation, demonstrating the benefit of using anchored feature parameterizations for VINS.
2026-06-16
arXiv (preprint)
doi.org
arxiv.org
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 … (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.
2020-06-24
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org