In this paper we study some modifications of the
algorithm. The case studied is feature
based mobile robot localization in a large scale environment.
The required sample set size for making
algorithm converge properly can
in many cases require too much computation. This is
often the case when observing features in symmetric
environments like for instance doors in long corridors.
In such areas a large sample set is required to resolve
the generated multi-hypotheses problem. To manage
with a sample set size which in the normal case would
algorithm to break down,
we study two modifications. The first strategy, called
with random sampling", takes part
of the sample set and spreads it randomly over the
environment the robot operates in. The second strategy,
with planned sampling",
places part of the sample set at planned positions based
on the detected features. From the experiments we conclude
that the second strategy is the best and can reduce
the sample set size by at least a factor of 40.
2000. 2518-2524 p.
Proc. of the IEEE International Conference on Robotics and Automation (ICRA’00)