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空间非合作旋转目标的模型重建与位姿优化.docx

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空间非合作旋转目标的模型重建与位姿优化 Abstract: Inrecentyears,theproblemofmodelreconstructionandposeoptimizationofnon-cooperativerotatingtargetsinspacehasattractedmuchattention.Thispaperaimstoproposeaframeworkforreconstructingthe3Dmodelofanon-cooperativerotatingtargetfromsparsepointclouddataobtainedbyspacebornesensors.Additionally,aposeoptimizationalgorithmisintroducedtorefinetheestimatedposeofthetargetusingiterativeclosestpoint(ICP)method.Theproposedframeworkisvalidatedthroughsimulationsandexperiments,showingitseffectivenessinmodelreconstructionandposeoptimization. 1.Introduction Withtherapiddevelopmentofspaceexploration,thereisanincreasingdemandforunderstandingandmonitoringnon-cooperativerotatingtargetsinspace.Thesetargets,suchasspacedebrisorenemysatellites,posegreatchallengesfortrackingandrecognitionduetotheirunpredictablemotion.Modelreconstructionandposeoptimizationarecriticalforunderstandingtheshapeandpositionofthesetargets,whichcanaidintrackingandmonitoringthem. 2.RelatedWorks Manytechniqueshavebeenproposedformodelreconstructionandposeestimationofnon-cooperativerotatingtargets.Someapproachesutilizelaserrangefindersorstructuredlightsystemstocapture3Dinformationofthetargets.Othersrelyonvision-basedmethodsusingcamerasorimagingsensors.Additionally,techniquessuchasphotogrammetryandtomographyhavebeenappliedtoreconstruct3Dmodels.However,allthesemethodshavelimitationsintermsofaccuracy,efficiency,orapplicabilityinspacebornescenarios. 3.FrameworkforModelReconstruction Inthispaper,aframeworkformodelreconstructionofnon-cooperativerotatingtargetsusingsparsepointclouddataisproposed.Theframeworkconsistsofthreemainsteps:pre-processing,registration,andsurfacereconstruction.Firstly,theacquiredpointclouddataispre-processedtoremoveoutliersandnoise.Then,registrationisperformedtoalignthepointclouddatafromdifferentviews.Finally,asurfacereconstructionalgorithmisappliedtogeneratea3Dmodelfromtheregisteredpointclouddata.Anevaluationmetricisintroducedtoassessthequalityofthereconstructedmodel. 4.PoseOptimi