Patrick Beeson, Aniket Murarka, and Benjamin Kuipers. 2006.

**Adapting proposal
distributions for accurate, efficient mobile robot
localization**.

IEEE International Conference on
Robotics and Automaton (ICRA-06).

#### Abstract

When performing probabilistic localization using a
particle filter, a robot must have a good proposal distribution in
which to distribute its particles. Once weighted by their normalized
likelihood scores, these particles estimate a posterior distribution
of the robot's possible poses.

This paper 1) introduces a new action model (the system of equations
used to determine the proposal distribution at each time step) that
can run on any differential drive robot, even from log file data, 2)
investigates the results of different algorithms that modify the
proposal distribution at each time step in order to obtain more
accurate localization, 3) investigates the results of incrementally
adapting the action model parameters based on recent localization
results in order to obtain efficient proposal distributions that
better approximate the true posteriors.

The results show that by adapting the action model over time and, when
necessary, modifying the resulting proposal distributions at each time
step, localization improves---the maximum likelihood score increases
and, when possible, the percentage of wasted particles decreases.

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