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一种大视场立体视觉标定方法.docx

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一种大视场立体视觉标定方法 Abstract Inrecentyears,largefieldofviewstereoscopicvisionsystemhasbeenwidelyusedinvariousfields,suchasmedicalimaging,entertainment,andautonomousvehicles.However,calibrationofthesystemremainsachallengingtask.Inthispaper,weproposeanovelcalibrationmethodforlargefieldofviewstereoscopicvisionsystem,whichcanobtainaccurateandrobustcalibrationresults.Theproposedmethodutilizesaphase-shiftingtechniquetoobtaintherelativeposesbetweenthecameras,andthenperformscalibrationbyminimizingthereprojectionerror.Experimentsonareal-worlddatasetdemonstratethattheproposedmethodoutperformsexistingstate-of-the-artmethodsintermsofaccuracyandrobustness. 1.Introduction Largefieldofviewstereoscopicvisionsystemshaveattractedincreasingattentioninvariousfields,suchasmedicalimaging,entertainment,andautonomousvehicles.Thesystemconsistsoftwoormorecameraswithwidefieldofview,whichcancapturestereoimagesandgenerate3Dmodelsoftheenvironment.Calibrationofthesystemisacriticalsteptoensuretheaccuracyofthe3Dreconstructionandtherobustnessofthesystem.However,calibrationoflargefieldofviewstereoscopicvisionsystemisachallengingtaskduetothecomplexcamerageometryandthenon-lineardistortion. Severalcalibrationmethodshavebeenproposedintheliterature.Generally,themethodsfallintotwocategories:feature-basedandnon-feature-basedmethods.Feature-basedmethodsutilizedistinctivefeaturesintheimage,suchascornersoredges,toestimatethecameraposes.Non-feature-basedmethodsdirectlyestimatethecameraposesbyfittingageometricmodeltotheimage.Thesemethodshaveachievedgoodaccuracyandrobustnessinsmallfieldofviewstereosystems,buttheybecomelesseffectiveinlargefieldofviewsystemsduetothedifficultyoffeaturedetectionandthecomplexgeometricmodel. Inthispaper,weproposeanovelcalibrationmethodforlargefieldofviewstereoscopicvisionsystem.Theproposedmethodcombinestheadvantagesoffeature-basedandnon-feature-basedmethods,andcanobtainaccurateandrobustcalibrationresults.Themethodutilizesaphase-shiftingtechniquetoobtaintherelativeposesbetweenthecameras,andth