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R o b ot M a p p i ng

Learn about robot mapping and SLAM (Simultaneous Localization and Mapping), which involves modeling the environment and estimating the robot's location and building a map simultaneously. Explore the relevance and applications of SLAM in various industries such as home automation, surveillance, underwater exploration, and space navigation.

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R o b ot M a p p i ng

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  1. RobotMapping IntroductiontoRobotMapping Courtesy of CyrillStachniss

  2. WhatisRobotMapping? • Robot–adevice,thatmovesthroughtheenvironment • Mapping–modelingtheenvironment

  3. RelatedTerms StateEstimation Localization Mapping SLAM MotionPlanning Navigation

  4. WhatisSLAM? Computingtherobot’sposesandthemapoftheenvironmentatthesametime Localization:estimatingtherobot’slocation Mapping:buildingamap SLAM:buildingamapandlocalizingtherobotsimultaneously

  5. LocalizationExample Estimatetherobot’sposesgivenlandmarks

  6. MappingExample Estimatethelandmarksgiventherobot’sposes

  7. SLAMExample Estimatetherobot’sposesandthelandmarksatthesametime

  8. TheSLAMProblem SLAMisachicken-or-eggproblem: →amapisneededforlocalizationand →aposeestimateisneededformapping map localize

  9. SLAMisRelevant Itisconsideredafundamentalproblemfortrulyautonomousrobots SLAMisthebasisformostnavigationsystems map autonomousnavigation localize

  10. SLAMApplications SLAMiscentraltoarangeofindoor,outdoor,airandunderwaterapplicationsforbothmannedandautonomousvehicles. Examples: Athome:vacuumcleaner,lawnmower Air:surveillancewithunmannedairvehicles Underwater:reefmonitoring Underground:explorationofmines Space:terrainmappingforlocalization 10

  11. SLAMApplications Undersea Indoors Underground Space 11 CourtesyofEvolutionRobotics,H.Durrant-Whyte,NASA,S.Thrun

  12. SLAMShowcase–Mint CourtesyofEvolutionRobotics(nowiRobot) 12

  13. SLAMShowcase–EUROPA

  14. MappingFreiburgCSCampus

  15. DefinitionoftheSLAMProblem Given Therobot’scontrols Observations Wanted Mapoftheenvironment Pathoftherobot

  16. ProbabilisticApproaches • Uncertaintyintherobot’smotionsandobservations • Usetheprobabilitytheorytoexplicitlyrepresenttheuncertainty “Therobotisexactlyhere” “Therobotissomewherehere”

  17. IntheProbabilisticWorld Estimatetherobot’spathandthemap distribution path mapgiven observations controls

  18. GraphicalModel unknown observed unknown

  19. FullSLAMvs.OnlineSLAM FullSLAMestimatestheentirepath OnlineSLAMseekstorecoveronlythemostrecentpose

  20. GraphicalModelofOnlineSLAM

  21. WhyisSLAMaHardProblem? 1.Robotpathandmaparebothunknown 2.Mapandposeestimatescorrelated

  22. WhyisSLAMaHardProblem? Themappingbetweenobservationsandthemapisunknown Pickingwrongdataassociationscanhave catastrophicconsequences(divergence) Robotposeuncertainty

  23. TaxonomyoftheSLAMProblem Volumetricvs.feature-basedSLAM CourtesybyE.Nebot 25

  24. TaxonomyoftheSLAMProblem Topologicvs.geometricmaps

  25. TaxonomyoftheSLAMProblem Knownvs.unknowncorrespondence

  26. TaxonomyoftheSLAMProblem Staticvs.dynamicenvironments

  27. TaxonomyoftheSLAMProblem Smallvs.largeuncertainty

  28. TaxonomyoftheSLAMProblem Activevs.passiveSLAM ImagecourtesybyPetterDuvander

  29. TaxonomyoftheSLAMProblem Single-robotvs.multi-robotSLAM

  30. ApproachestoSLAM LargevarietyofdifferentSLAMapproacheshavebeenproposed MostroboticsconferencesdedicatemultipletrackstoSLAM Themajorityoftechniquesusesprobabilisticconcepts HistoryofSLAMdatesbacktothemid-eighties Relatedproblemsingeodesyandphotogrammetry

  31. SLAMHistorybyDurrant-Whyte 1985/86:Smithetal.andDurrant-Whytedescribegeometricuncertaintyandrelationshipsbetweenfeaturesorlandmarks 1986:DiscussionsatICRAonhowtosolvetheSLAMproblemfollowedbythekeypaperbySmith,SelfandCheeseman 1990-95:Kalman-filterbasedapproaches 1995:SLAMacronymcoinedatISRR’95 1995-1999:Convergenceproofs&firstdemonstrationsofrealsystems 2000:WideinterestinSLAMstarted

  32. ThreeMainParadigms Kalmanfilter Particlefilter Graph-based

  33. MotionandObservationModel "Motionmodel" "Observationmodel"

  34. MotionModel Themotionmodeldescribestherelativemotionoftherobot distribution newpose given oldpose control

  35. MotionModelExamples Gaussianmodel Non-Gaussianmodel

  36. StandardOdometryModel Robotmovesfromto Odometryinformation .

  37. MoreonMotionModels Course:IntroductiontoMobileRobotics,Chapter6 Thrunetal.“ProbabilisticRobotics”,Chapter5

  38. ObservationModel Theobservationorsensormodelrelatesmeasurementswiththerobot’spose distribution observation given pose

  39. ObservationModelExamples Gaussianmodel Non-Gaussianmodel

  40. MoreonObservationModels Course:IntroductiontoMobileRobotics,Chapter7 Thrunetal.“ProbabilisticRobotics”,Chapter6

  41. Summary Mappingisthetaskofmodelingtheenvironment Localizationmeansestimatingtherobot’spose SLAM=simultaneouslocalizationandmapping FullSLAMvs.OnlineSLAM RichtaxonomyoftheSLAMproblem

  42. Literature SLAMoverview Springer“HandbookonRobotics”,ChapteronSimultaneousLocalizationandMapping(subsection1&2) Onmotionandobservationmodels Thrunetal.“ProbabilisticRobotics”,Chapters5&6 Course:IntroductiontoMobileRobotics,Chapters6&7

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