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Found 9 papers, 2 papers with code
Consistent and Asymptotically Statistically-Efficient Solution to Camera Motion Estimation
Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community.
Kernel-based Regularized Iterative Learning Control of Repetitive Linear Time-varying Systems
For data-driven iterative learning control (ILC) methods, both the model estimation and controller design problems are converted to parameter estimation problems for some chosen model structures.
Consistent and Asymptotically Efficient Localization from Range-Difference Measurements
Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator.
On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification
For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping.
An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications
Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling.
Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator
Regularized system identification is the major advance in system identification in the last decade.
CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination
The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies.
Global and Asymptotically Efficient Localization from Range Measurements
In terms of whether the variance of measurement noises is known or not, we propose the Bias-Eli estimator (which involves solving a generalized trust region subproblem) and the Noise-Est estimator (which is obtained by solving a convex problem), respectively.
Identification of Switched Linear Systems: Persistence of Excitation and Numerical Algorithms
The main contribution is a much weaker condition on the regressor to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems.
