Factor Graphs for Robot Perception by Frank Dellaert

Factor Graphs for Robot Perception by Frank Dellaert

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.

SKU	Non disponible
ISBN 13	9781680833263
ISBN 10	168083326X
Titre	Factor Graphs for Robot Perception
Auteur	Frank Dellaert
Série	Foundations And Trends In Robotics
État	Non disponible
Type de reliure	Paperback
Éditeur	now publishers Inc
Année de publication	2017-08-15
Nombre de pages	154
Note de couverture	La photo du livre est présentée à titre d'illustration uniquement. La reliure, la couverture ou l'édition réelle peuvent varier.
Note	Non disponible