1 citations · 4 across the 13 of their papers we have counts for
14 papers
The Iterative Equivariant Filter
Pieter van Goor, James Richard Forbes
This paper presents the iterative equivariant filter (IterEqF). The iterative extended Kalman filter (IterEKF) replaces the standard EKF correction step with an iterative correctio…
ARC: Adaptive Robust Joint State and Covariance Estimation
Alexandre Hadji-Thomas, Andrew Stirling, James R. Forbes
Sensor measurements are frequently corrupted by outliers and non-Gaussian noise. These imperfections in the sensor data can cause classical state estimators to generate biased and…
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) that utilize anchored feature represent…
KILO-EKF: Koopman-Inspired Learned Observations Extended Kalman Filter
Zi Cong Guo, James R. Forbes, Timothy D. Barfoot
We present the Koopman-Inspired Learned Observations Extended Kalman Filter (KILO-EKF), which combines a standard EKF prediction step with a correction step based on a Koopman-insp…
Gaussian Variational Inference with Non-Gaussian Factors for State Estimation: A UWB Localization Case Study
Andrew Stirling, Mykola Lukashchuk, Dmitry Bagaev +2
This letter extends the exactly sparse Gaussian variational inference (ESGVI) algorithm for state estimation in two complementary directions. First, ESGVI is generalized to operate…
Nonlinear Observer Design for Visual-Inertial Odometry
Mouaad Boughellaba, Abdelhamid Tayebi, James R. Forbes +1
This paper addresses the problem of Visual-Inertial Odometry (VIO) for rigid body systems evolving in three-dimensional space. We introduce a novel matrix Lie group structure, deno…
dkpy: Robust Control with Structured Uncertainty in Python
Timothy Everett Adams, Steven Dahdah, James Richard Forbes
Models used for control design are, to some degree, uncertain. Model uncertainty must be accounted for to ensure the robustness of the closed-loop system. -analysis and -synt…
Design of Input-Output Observers for a Population of Systems with Bounded Frequency-Domain Variation using -iteration
Timothy Everett Adams, James Richard Forbes
This paper proposes a linear input-output observer design methodology for a population of systems in which each observer uses knowledge of the linear time-invariant dynamics of the…
Discretization of Linear Systems using the Matrix Exponential
Steven Dahdah, James Richard Forbes
Discretizing continuous-time linear systems typically requires numerical integration. This document presents a convenient method for discretizing the dynamics, input, and process n…
Globally Optimal Data-Association-Free Landmark-Based Localization Using Semidefinite Relaxations
Vassili Korotkine, Mitchell Cohen, James Richard Forbes
This paper proposes a semidefinite relaxation for landmark-based localization with unknown data associations in planar environments. The proposed method simultaneously solves for t…
Uncertainty Modelling and Robust Observer Synthesis using the Koopman Operator
Steven Dahdah, James Richard Forbes
This paper proposes a robust nonlinear observer synthesis method for a population of systems modelled using the Koopman operator. The Koopman operator allows nonlinear systems to b…
Asymptotically Stable Data-Driven Koopman Operator Approximation with Inputs using Total Extended DMD
Louis Lortie, James Richard Forbes
The Koopman operator framework can be used to identify a data-driven model of a nonlinear system. Unfortunately, when the data is corrupted by noise, the identified model can be bi…
A Hessian for Gaussian Mixture Likelihoods in Nonlinear Least Squares
Vassili Korotkine, Mitchell Cohen, James Richard Forbes
This paper proposes a novel Hessian approximation for Maximum a Posteriori estimation problems in robotics involving Gaussian mixture likelihoods. Previous approaches manipulate th…
Forward-Backward Extended DMD with an Asymptotic Stability Constraint
Louis Lortie, Steven Dahdah, James Richard Forbes
This paper presents a data-driven method to identify an asymptotically stable Koopman system from noisy data. In particular, the proposed approach combines approximations of the sy…