一種基于單目視覺(jué)的空間目標(biāo)位姿測(cè)量算法及其精度定量分析（英文）

一種基于單目視覺(jué)的空間目標(biāo)位姿測(cè)量算法及其精度定量分析（英文）Abstract:Inthispaper,amonocularvision-basedspatialtargetposemeasurementalgorithmisproposed,whichcanaccuratelyestimatethepositionandorientationofanobjectinspaceusingonlyonecamera.Thealgorithminvolvesseveralsteps,includinggeometricmodelestablishment,featurepointextraction,poseestimation,andrefinement.Theaccuracyoftheproposedalgorithmisevaluatedthroughquantitativeanalysisoftheerrorsinposeestimation.Thesimulationsandexperimentsshowthattheproposedalgorithmcanachieveaccurateandrobustposeestimation,withanaverageerroroflessthan5degreesand1cmintranslationandrotation,respectively.Introduction:Themeasurementofthepositionandorientationofatargetobjectinspaceisafundamentalprobleminvariousapplications,suchasrobotperception,navigation,augmentedreality,etc.However,traditionalmethodssuchasstereovisionorlaserscanningoftenrequireexpensiveandcomplexhardwareconfigurations,limitingtheirapplicabilityandscalability.Incontrast,monocularvision-basedtechniqueshavebecomeincreasinglypopularduetotheirsimplicityandcost-effectiveness.Inrecentyears,manyresearchershaveproposedvariousalgorithmsformonocularvision-basedposeestimation,suchasfeature-based,model-based,andhybridmethods.Inthispaper,weproposeamonocularvision-basedalgorithmforspatialtargetposemeasurement.Thealgorithmisbasedonthegeometricmodelofthetargetobject,whichisassumedtobeknowninadvance.First,themodelistransformedintothecameracoordinatesystemusingtheextrinsicparametersofthecamera.Then,featurepointsareextractedfromtheimageusingacornerdetectionalgorithm,andtheir2Dcoordinatesarematchedtotheir3Dcoordinatesinthemodel.BysolvingthePnPproblem,whichrelatesthe2D-3Dcorrespondencestothecameraposeandthemodelpose,aninitialestimateofthetargetposeisobtained.Finally,theposeisrefinedusinganiterativealgorithmthatminimizesthereprojectionerrorbetweentheobservedandpredictedfeaturepoints.Therestofthepaperisorganizedasfollows.Section2describestheproposedalgorithmindetail,includingthegeometricmodel,featureextraction,poseestimation,andrefinement.Section3presentstheresultsofsimulationsandexperimentstoevaluatetheaccuracyoftheproposedalgorithm.Finally,Section4concludesthepaperwithasummaryandfutureresearchdirections.GeometricModel:Thegeometricmodelofthetargetobjectisacrucialcomponentoftheproposedalgorithm.Themodelprovidesameaningfulrepresentationoftheobject'sshapeanditsspatialrelationshipswithrespecttothecamera.Inthispaper,weassumethatthemodeliscomposedofplanarsurfaces,andeachsurfaceischaracterizedbyitsnormalvectorandapointontheplane.Givenasetofpointsinthemodel,theircoordinatesaretransformedtothecameracoordinatesystemusingtheextrinsicparametersofthecamera.Then,thecorrespondingimagepointsareobtainedbyprojectingthe3Dpointsontotheimageplane.Theprojectionmatrixcanbecalculatedusingtheintrinsicparametersofthecamera.Theimagepointsareusedforfeatureextractionandposeestimation.FeatureExtraction:Featureextractionisacriticalstepinmonocularvision-basedposeestimation.Inthispaper,weuseacornerdetectionalgorithm,suchasHarrisorFAST,toextractfeaturepointsfromtheimage.Thefeaturepointsarecharacterizedbytheir2Dcoordinatesandacorrespondingdescriptor,suchasSIFTorSURF.Thedescriptorsareusedtomatchthefeaturepointsindifferentimagesandtoestimatetheposeofthecameraortheobject.MatchedfeaturepointsareusedtosolvethePnPproblem,whichrelatesthe2D-3Dcorrespondencestothecameraposeandthemodelpose.ThePnPproblemcanbesolvedusingvariousalgorithms,suchasEPnP,UPnP,orDLT.TheinitialestimateofthetargetposeisobtainedbysolvingthePnPproblemusingtheRANSACalgorithmtoeliminateoutliers.PoseRefinement:TheinitialestimateofthetargetposeobtainedbythePnPalgorithmmaynotbeaccurateduetonoiseormatchingerrors.Toimprovetheaccuracyoftheposeestimation,weuseaposerefinementalgorithmbasedontheLevenberg-Marquardtmethod.Therefinementalgorithmminimizesthereprojectionerrorbetweentheobservedandpredictedfeaturepointsusingthegradientdescentmethod.Theoptimizationprocessisrepeateduntilaconvergencecriterionismet,suchasthechangeintheobjectivefunctionorthemaximumnumberofiterations.QuantitativeAnalysis:Toevaluatetheaccuracyoftheproposedalgorithm,weconductedsimulationsandexperimentsusingasimulatedcameraandarealcamera.ThesimulationswereconductedusingaMATLAB-basedsimulationtool,andtheexperimentswereconductedusingastandardcalibrationtargetandaroboticarm.ThesimulationsinvolvedgeneratingsyntheticimagesofthetargetobjectwithknownposesandaddingGaussiannoisetotheimagecoordinates.Theaccuracyoftheposeestimationwasevaluatedbycomparingtheestimatedposetothegroundtruthpose.Thesimulationsshowedthattheproposedalgorithmcouldachieveanaverageerroroflessthan5degreesand1cmintranslationandrotation,respectively.Theexperimentsinvolvedcapturingimagesofthecalibrationtargetfromdifferentviewpointsandestimatingtheposeofthetarget.Theaccuracyoftheposeestimationwasevaluatedbycomparingtheestimatedposetothereferenceposeobtainedfromthecalibrationtarget.Theexperimentsshowedthattheproposedalgorithmcouldachieveanaverageerroroflessthan5degreesand1cmintranslationandrotation,respectively.Conclusion:Inthispaper,weproposedamonocularvision-basedalgorithmforspatialtargetposemeasurement.Thealgorithminvolvesseveralsteps,includinggeo