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On the way to achieving higher degrees of autonomy for vehicles in complicated, ever changing scenarios, the localization problem poses a very important role. Especially the Simultaneous Localization and Mapping (SLAM) problem has been studied greatly in the past. For an autonomous system in the real world, we present a very cost-efficient, robust and very precise localization approach based on GraphSLAM and graph optimization using radar sensors. We are able to prove on a dynamically changing parking lot layout that both mapping and localization accuracy are very high. To evaluate the performance of the mapping algorithm, a highly accurate ground truth map generated from a total station was used. Localization results are compared to a high precision DGPS/INS system. Utilizing these methods, we can show the strong performance of our algorithm.
Um einen reibungslosen Produktionsablauf zu gewährleisten und Produktionsstillstände zu vermeiden, ist eine kontinuierliche Materialverfügbarkeit erforderlich. Bei der Auswahl von Materialbereitstellungsprinzipien gilt es, unternehmensspezifische Gegebenheiten zu berücksichtigen. Der vorliegende Beitrag zeigt am Beispiel der ERBE Elektromedizin GmbH, Tübingen, ein mögliches Vorgehen.