
Factor Graphs for Robot Perception
now publishers Inc
1st Edition
Published on 15. August 2017
Book
Paperback/Softback
154 pages
978-1-68083-326-3 (ISBN)
Description
Factor Graphs for Robot Perception reviews the use of factor graphs for the modeling and solving of large-scale inference problems in robotics. Factor graphs are a family of probabilistic graphical models, other examples of which are Bayesian networks and Markov random fields, well known from the statistical modeling and machine learning literature. They provide a powerful abstraction that gives insight into particular inference problems, making it easier to think about and design solutions, and write modular software to perform the actual inference. This book illustrates their use in the simultaneous localization and mapping problem and other important problems associated with deploying robots in the real world. Factor graphs are introduced as an economical representation within which to formulate the different inference problems, setting the stage for the subsequent sections on practical methods to solve them. The book explains the nonlinear optimization techniques for solving arbitrary nonlinear factor graphs, which requires repeatedly solving large sparse linear systems.
Factor Graphs for Robot Perception will be of interest to students, researchers and practicing roboticists with an interest in the broad impact factor graphs have had, and continue to have, in robot perception.
Factor Graphs for Robot Perception will be of interest to students, researchers and practicing roboticists with an interest in the broad impact factor graphs have had, and continue to have, in robot perception.
More details
Series
Language
English
Place of publication
Hanover
United States
Target group
College/higher education
Dimensions
Height: 264 mm
Width: 156 mm
ISBN-13
978-1-68083-326-3 (9781680833263)
DOI
10.1561/2300000043
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Content
1: Introduction 2: Smoothing and Mapping 3: Exploiting Sparsity 4: Elimination Ordering 5: Incremental Smoothing and Mapping 6: Optimization on Manifolds 7: Applications. Acknowledgements. References.