Abstract
This paper explores how sensor and motion modeling can be improved
to better Markov localization by exploiting deviations from expected
sensor readings. Proprioception is achieved by monitoring target
and actual motions of robot joints. This provides information about
whether or not an action was executed as desired, yielding a quality
measure of the current odometry. Odometry is usually extremely prone
to errors for legged robots, especially in dynamic environments
where collisions are often unavoidable, due to the many degrees
of freedom of the robot and the numerous possibilities of motion
hindrance. A quality measure helps differentiate the periods of
unhindered motion from periods where robot motion was impaired for
whatever reason. Negative evidence is collected when a robot fails
to detect a landmark that it expects to see. Therefore the gaze
direction of the camera has to be modeled accordingly. This enables
the robot to localize where it could not when only using landmarks.
In the general localization task, the probability distribution converges
more quickly when negative information is taken into account.
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