Deep inference for covariance estimation: Learning gaussian noise models for state estimation
We present a novel method of measurement covariance estimation that models
measurement uncertainty as a function of the measurement itself. Existing work in predictive …
measurement uncertainty as a function of the measurement itself. Existing work in predictive …
Nonparametric Bayesian inference on multivariate exponential families
We develop a model by choosing the maximum entropy distribution from the set of models
satisfying certain smoothness and independence criteria; we show that inference on this …
satisfying certain smoothness and independence criteria; we show that inference on this …
Learning Gaussisan noise models from high-dimensional sensor data with deep neural networks
KY Liu - 2018 - dspace.mit.edu
While measurement covariances are often taken to be constant in many robotic state
estimation systems, many sensors exhibit different interactions with their environment …
estimation systems, many sensors exhibit different interactions with their environment …
Probabilistic graphical models: distributed inference and learning models with small feedback vertex sets
Y Liu - 2014 - dspace.mit.edu
In undirected graphical models, each node represents a random variable while the set of
edges specifies the conditional independencies of the underlying distribution. When the …
edges specifies the conditional independencies of the underlying distribution. When the …
Reconstruction 3D d'environnements intérieurs à partir d'acquisitions LiDAR
J Sanchez - 2020 - theses.hal.science
Ce travail de thèse porte sur la reconstruction 3D d'environnements structurés à partir
d'acquisitions LiDAR. L'étude a pour but d'automatiser et d'améliorer la chaîne de …
d'acquisitions LiDAR. L'étude a pour but d'automatiser et d'améliorer la chaîne de …