In this paper we analyze hybrid scan matching algorithms and we test their performances in typical mobile applica- tions. Since the genetic algorithm is robust but not very accurate, and ICP is accurate but not very robust, it is nat- ural to use the two algorithms in a cascade fashion: rst we run a genetic optimization to nd an approximate but ro- bust matching solution and then we run the Iterative Clos- est Point (ICP) algorithm to increase the accuracy. The proposed genetic algorithm is very fast due to a look-up ta- ble 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 slip- page or when closing loops. We show experimentally that the proposed algorithm successfully copes with large local- ization errors.

An Effective and Efficient Hybrid Scan Matching Algorithm for Mobile Object Applications

CUZZOCREA, Alfredo Massimiliano;MUMOLO, ENZO
2017

Abstract

In this paper we analyze hybrid scan matching algorithms and we test their performances in typical mobile applica- tions. Since the genetic algorithm is robust but not very accurate, and ICP is accurate but not very robust, it is nat- ural to use the two algorithms in a cascade fashion: rst we run a genetic optimization to nd an approximate but ro- bust matching solution and then we run the Iterative Clos- est Point (ICP) algorithm to increase the accuracy. The proposed genetic algorithm is very fast due to a look-up ta- ble 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 slip- page or when closing loops. We show experimentally that the proposed algorithm successfully copes with large local- ization errors.
978-1-4503-4486-9
https://dl.acm.org/citation.cfm?id=3019720&CFID=837506390&CFTOKEN=38429089
File in questo prodotto:
File Dimensione Formato  
front matter+ cuzzocrea.pdf

non disponibili

Tipologia: Documento in Versione Editoriale
Licenza: Digital Rights Management non definito
Dimensione 6.63 MB
Formato Adobe PDF
6.63 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2898173
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact