Robust and accurate genetic scan matching algorithm for robotic navigation

Abstract

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. © 2011 Springer-Verlag.