Skip to main navigation
Skip to search
Skip to main content
University of Copenhagen Research Portal Home
Help & FAQ
Dansk
English
Home
Profiles
Research output
Research units
Press/Media
Activities
Prizes
???studenttheses???
Datasets
Search by expertise, name or affiliation
Unscented Kalman filtering on Riemannian manifolds
Søren Hauberg,
Francois Bernard Lauze
,
Kim Steenstrup Pedersen
Department of Computer Science
49
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Unscented Kalman filtering on Riemannian manifolds'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Unscented Kalman Filtering
100%
Tracking Problem
66%
Euclidean Domain
33%
Gauss-Newton Method
33%
Unscented Transformation
33%
Brute Force
33%
Pose Optimization
33%
Sequential Data Analysis
33%
Region Tracking
33%
Covariance Features
33%
Unscented Kalman Filter
33%
Value Optimization
33%
Data State
33%
Non-Euclidean
33%
Monte Carlo Filter
33%
Engineering
Kalman Filter
100%
Filtration
100%
Newton's Method
33%
Gauss
33%
Illustrates
33%
Mean Value
33%
State Variable
33%
Observed Data
33%
Optimisation Problem
33%
Mathematics
Euclidean Domain
25%
Observed Data
25%
Hidden State
25%
Gauss-Newton Method
25%