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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.
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