1 citations · 4 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…