In this paper we propose a scan matching algorithm for
robotic navigation based on the combination of ICP and genetic optimization.
Since the genetic algorithm is robust but not very accurate,
and ICP is accurate but not very robust, it is natural to use the two
algorithms in a cascade fashion: first we run a genetic optimization to
find an approximate but robust matching solution and then we run ICP
to increase accuracy. The proposed genetic algorithm is very fast due to
a lookup table formulation and very robust against large errors in both
distance and angle during scan data acquisition. It is worth mentioning
that large scan errors arise very commonly in mobile robotics due, for
instance, to wheel slippage. We show experimentally that the proposed
algorithm successfully copes with large localization errors.
