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	Unavailable
ISBN 13	9781680833263
ISBN 10	168083326X
Title	Factor Graphs for Robot Perception
Author	Frank Dellaert
Series	Foundations And Trends In Robotics
Condition	Unavailable
Binding Type	Paperback
Publisher	now publishers Inc
Year published	2017-08-15
Number of pages	154
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable