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Robot localization is the problem of how to estimate a robot’s pose within an objective
frame of reference. Traditional localization requires knowledge of two key conditional
probabilities: the motion and sensor models. These models depend critically on the specific
robot as well as its environment. Building these models can be time-consuming, manually
intensive, and can require expert intuitions. However, the models are necessary for the robot
to relate its own subjective view of sensors and motors to the robot’s objective pose. In this
paper we seek to remove the need for human provided models. We introduce a technique for
subjective localization, relaxing the requirement that the robot localize within a global frame
of reference. Using an algorithm for action-respecting non-linear dimensionality reduction,
we learn a subjective representation of pose from a stream of actions and sensations.We then
extract from the data natural motion and sensor models defined for this new representation.
Monte Carlo localization is used to track this representation of the robot’s pose while executing
new actions and receiving new sensor readings. We evaluate the technique in a synthetic
image manipulation domain and with a mobile robot using vision and
laser sensors.

-- Main.simra - 26 Sep 2005
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Topic revision: r1 - 2005-09-26 - RobertSim