Autonomous navigation is an essential capability for mobile robots.
In order to operate robustly, a robot needs to know what the
environment looks like, where it is in its environment, and how to
navigate within it. Particle Filters for Robot Navigation
summarizes approaches that address these three problems and that
use particle filters as their main underlying model for
representing beliefs. It illustrates that these filters are
powerful tools that can robustly estimate the state of the robot
and its environment and that they are also well-suited to making
decisions about how to navigate in order to minimize a robot's
uncertainty about its own position and the state of the
environment. They offer a series of attractive capabilities,
including the ability to deal with non-Gaussian distributions and
nonlinear sensor and motion models. Particle filters have been used
for almost twenty years in robotics and have become a standard
means for a series of tasks. This is an ideal primer for anyone
interested in the research and application of these filters in
robotics.
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