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RANSAM for Industrial Bin-Picking
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2010
Year
EngineeringField RoboticsPoint Cloud ProcessingLocalizationMaterial HandlingImage AnalysisExible Bin-picking SystemPattern RecognitionImage RegistrationSystems EngineeringCombinatorial OptimizationComputational GeometryIndustrial Bin-pickingGeometric ModelingMachine VisionComputer Science3D Object RecognitionComputer VisionSpatial VerificationIndustrial DesignAdapted Localization AlgorithmNatural SciencesRandom Sample Matching3D ScanningIndustrial InformaticsRobotics
In this paper we present a highly ??exible bin-picking system that can handle nearly any object to be picked out of a box. The objects are localized by matching CAD data with a laser scan. The adapted localization algorithm is based on the Random Sample Matching (RANSAM) approach formerly developed at our institute for registration of two arbitrary surface fragments, which turned out to be very robust against sensor noise. Experimental results demonstrate the characteristics and the good performance of the proposed approach and thus prove its great potential in the ??eld of bin-picking.