An effective and efficient hybrid scan matching algorithm for mobile object applications

Abstract

In this paper we analyze hybrid scan matching algorithms and we test their performances in typical mobile applications. 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 the Iterative Closest Point (ICP) algorithm to increase the accuracy. The proposed genetic algorithm is very fast due to a look-up 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 object applications due, for instance, to wheel slippage or when closing loops. We show experimentally that the proposed algorithm successfully copes with large localization errors.