The Resource Bayesian inference for dynamic pose estimation using directional statistics, by Jacob E. Darling

Bayesian inference for dynamic pose estimation using directional statistics, by Jacob E. Darling

Label
Bayesian inference for dynamic pose estimation using directional statistics
Title
Bayesian inference for dynamic pose estimation using directional statistics
Statement of responsibility
by Jacob E. Darling
Creator
Author
Subject
Language
eng
Summary
"The dynamic pose of an object, where the object can represent a spacecraft, aircraft, or mobile robot, among other possibilities, is defined to be the position, velocity, attitude, and angular velocity of the object. A new method to perform dynamic pose estimation is developed that leverages directional statistics and operates under the Bayesian estimation framework, as opposed to the minimum mean square error (MMSE) framework that conventional methods employ. No small attitude uncertainty assumption is necessary using this method, and, therefore, a more accurate estimate of the state can be obtained when the attitude uncertainty is large. Two new state densities, termed the Gauss-Bingham and Bingham-Gauss mixture (BGM) densities, are developed that probabilistically represent a state vector comprised of an attitude quaternion and other Euclidean states on their natural manifold, the unit hypercylinder. When the Euclidean states consist of position, velocity, and angular velocity, the state vector represents the dynamic pose. An uncertainty propagation scheme is developed for a Gauss-Bingham-distributed state vector, and two demonstrations of this uncertainty propagation scheme are presented that show its applicability to quantify the uncertainty in dynamic pose, especially when the attitude uncertainty becomes large. The BGM filter is developed, which is an approximate Bayesian filter in which the true temporal and measurement evolution of the BGM density, as quantified by the Chapman-Kolmogorov equation and Bayes' rule, are approximated by a BGM density. The parameters of the approximating BGM density are found via integral approximation on a component-wise basis, which is shown to be the Kullback-Leibler divergence optimal parameters of each component. The BGM filter is then applied to three simulations in order to compare its performance to a multiplicative Kalman filter and demonstrate its efficacy in estimating dynamic pose. The BGM filter is shown to be more statistically consistent than the multiplicative Kalman filter when the attitude uncertainty is large"--Abstract, page iii
Member of
Cataloging source
UMR
http://library.link/vocab/creatorName
Darling, Jacob E
Degree
Ph. D.
Dissertation year
2016
Granting institution
Missouri University of Science and Technology
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/subjectName
  • Artificial satellites
  • Space vehicles
  • Mobile robots
  • Bayesian statistical decision theory
  • Navigation
Label
Bayesian inference for dynamic pose estimation using directional statistics, by Jacob E. Darling
Instantiates
Publication
Note
Vita
Bibliography note
Includes bibliographic references (pages 209-214)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
974710050
Extent
1 online resource (xi, 215 pages)
Form of item
online
Governing access note
These materials are protected under copyright by the original author
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (most colored).
Specific material designation
remote
System control number
(OCoLC)974710050
Label
Bayesian inference for dynamic pose estimation using directional statistics, by Jacob E. Darling
Publication
Note
Vita
Bibliography note
Includes bibliographic references (pages 209-214)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
974710050
Extent
1 online resource (xi, 215 pages)
Form of item
online
Governing access note
These materials are protected under copyright by the original author
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (most colored).
Specific material designation
remote
System control number
(OCoLC)974710050

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