Factor Graphs for Robot Perception
Series: Foundations and Trends? in Robotics; 18;
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Product details:
- Publisher Now Publishers
- Date of Publication 15 August 2017
- Number of Volumes Paperback
- ISBN 9781680833263
- Binding Paperback
- No. of pages162 pages
- Size 234x156x9 mm
- Weight 238 g
- Language English 0
Categories
Short description:
Reviews the use of factor graphs for the modelling and solving of large-scale inference problems in robotics. This book illustrates their use in the simultaneous localization and mapping problem and other important problems associated with deploying robots in the real world.
MoreLong 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. It 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
44 430 HUF
40 876 HUF
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