Austin Eliazar's doctoral dissertation

156 Pages·2012·1.56 MB·English

Copyright (cid:0) c 2005 by Austin Eliazar
All rights reserved
DP-SLAM
by
Austin Eliazar
DepartmentofComputerScience
DukeUniversity
Date:
Approved:
RonaldParr,Supervisor
PankajAgarwal
Larry Carin
CarloTomasi
Dissertationsubmittedinpartial fulfillmentofthe
requirementsforthedegreeofDoctorofPhilosophy
in theDepartment ofComputerScience
in theGraduateSchoolof
DukeUniversity
2005
ABSTRACT
DP-SLAM
by
Austin Eliazar
DepartmentofComputerScience
DukeUniversity
Date:
Approved:
RonaldParr,Supervisor
PankajAgarwal
Larry Carin
CarloTomasi
An abstractofadissertationsubmittedinpartial
fulfillmentofthe requirementsforthe degree
ofDoctorofPhilosophyinthe Departmentof
ComputerSciencein theGraduateSchoolof
DukeUniversity
2005
Abstract
We present a novel, laser range finder based algorithm for simultaneous localization and
mapping (SLAM) for mobile robots. SLAM addresses the problem of constructingan ac-
curate map in real time despite imperfect information about the robot’s trajectory through
theenvironment. Unlikeotherapproachesthatassumepredeterminedlandmarks(andmust
deal with a resulting data-association problem) our algorithm uses the sensor range data
directly to build metric occupancy maps. Our algorithm uses a particle filter to represent
both robot poses and possible map configurations. By using a new map representation,
which we call distributedparticle (DP) mapping, we are able to maintain and update hun-
dreds of candidate maps and robot poses efficiently. Through careful implementation, we
are able to achieve a time complexity which is linear in both the area observed and the
number of particles used. Our technique contains essentially no assumptions about the
environment yet it is accurate enough to close loops over 100m in length with crisp, per-
pendicular edges on corridors and minimal or no misalignment errors, despite significant
noiseandambiguity.
iv
Contents
Abstract iv
ListofTables ix
ListofFigures x
Acknowledgements xiv
1 Introduction 1
1.1 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Previous Work 5
2.1 LocalizationOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 ParticleFilterReview . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 ParticleFilters forLocalization . . . . . . . . . . . . . . . . . . 6
2.2 LandmarkSLAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 Kalman FilterOverview . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2 Kalman FiltersforSLAM . . . . . . . . . . . . . . . . . . . . . 11
2.2.3 Variationson LandmarkSLAM . . . . . . . . . . . . . . . . . . 12
2.3 Hybrid TopologicalSLAM . . . . . . . . . . . . . . . . . . . . . . . . . 15
3 Map Representation 17
3.1 Occupancy Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2 Deterministic andStochasticOccupancyModels . . . . . . . . . . . . . 18
3.3 Laser Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.4 Map RepresentationandObservationModel . . . . . . . . . . . . . . . . 20
3.5 Map Updates& ObservationModel . . . . . . . . . . . . . . . . . . . . 24
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4 DP-SLAM 26
4.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1.1 Single Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1.2 AncestryTrees . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.1.3 Maintainingthe ParticleAncestryTree . . . . . . . . . . . . . . 31
4.1.4 DP-MapRepresentation . . . . . . . . . . . . . . . . . . . . . . 36
4.1.5 SLAM usingaDP-Map . . . . . . . . . . . . . . . . . . . . . . 38
4.2 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2.1 NaiveImplementation . . . . . . . . . . . . . . . . . . . . . . . 39
4.2.2 InitialAnalysisofDP-SLAM . . . . . . . . . . . . . . . . . . . 40
4.2.3 EmpiricalEvaluationofDP-SLAM . . . . . . . . . . . . . . . . 43
5 LinearTime Complexity 53
5.1 Map DataStructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2 Ancestrytreenodedatastructure . . . . . . . . . . . . . . . . . . . . . . 54
5.3 Map cachedatastructure . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.4 Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.5 Deletions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.6 Summary ofComputationalComplexity . . . . . . . . . . . . . . . . . . 59
5.7 ImplementationandEmpiricalResults . . . . . . . . . . . . . . . . . . . 60
6 MotionModelsand ProposalDistributions 62
6.1 OtherProposalDistributionImprovements . . . . . . . . . . . . . . . . . 62
6.2 Previous CalibrationMethods . . . . . . . . . . . . . . . . . . . . . . . 64
6.3 Motion ModelDetails . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.4 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
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6.5 EmpiricalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
7 Coalescence 81
7.1 EmpiricalBehavior ofCoalescence . . . . . . . . . . . . . . . . . . . . . 81
7.2 ImplicationsofCoalescence . . . . . . . . . . . . . . . . . . . . . . . . 84
8 HierarchicalSLAM 86
8.1 Drift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
8.2 Hierarchical SLAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
8.2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
8.2.2 Hierarchical Algorithm . . . . . . . . . . . . . . . . . . . . . . . 89
8.3 ImplementationandEmpiricalResults . . . . . . . . . . . . . . . . . . . 92
8.4 Extensions ofHierarchalSLAM . . . . . . . . . . . . . . . . . . . . . . 97
9 PracticalImprovements 98
9.1 Culling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
9.2 ImportantParameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
9.2.1 ObservationModel . . . . . . . . . . . . . . . . . . . . . . . . . 100
9.2.2 Motion Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
9.2.3 Map Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 101
9.2.4 Hierarchical Parameters . . . . . . . . . . . . . . . . . . . . . . 103
10 Summary of2-DDP-SLAM 104
11 3-DSLAM 106
11.1 Preliminary 3-DSLAMWork . . . . . . . . . . . . . . . . . . . . . . . 106
11.2 TechnicalIssues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
11.2.1 ProposalDistribution/ MotionEstimation . . . . . . . . . . . . . 108
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11.2.2 ObservationDependence . . . . . . . . . . . . . . . . . . . . . . 109
11.3 DataExplosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
11.3.1 3-DMappingwithVoxels . . . . . . . . . . . . . . . . . . . . . 111
11.3.2 Localization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
11.3.3 Map Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
11.4 ComputationComplexity . . . . . . . . . . . . . . . . . . . . . . . . . . 121
11.5 InitialResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
12 Future Directions 127
12.1 AlternateSensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
12.1.1 AdaptingBetterStereo Vision . . . . . . . . . . . . . . . . . . . 128
12.2 ProposalDistributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
12.2.1 AdaptiveParticleNumbers . . . . . . . . . . . . . . . . . . . . . 129
12.3 AlternativeMapRepresentations . . . . . . . . . . . . . . . . . . . . . . 130
12.3.1 QuadTrees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
12.3.2 VariableMap Resolution . . . . . . . . . . . . . . . . . . . . . . 132
12.3.3 SoftUpdates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
12.3.4 ImprovedPriors. . . . . . . . . . . . . . . . . . . . . . . . . . . 133
12.3.5 Spheres ofInfluence . . . . . . . . . . . . . . . . . . . . . . . . 134
12.4 ActiveSLAM andExploration . . . . . . . . . . . . . . . . . . . . . . . 135
12.5 PrincipledLoopClosing . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Bibliography 138
Biography 142
viii
List of Tables
5.1 Comparisonoftherunningtimesfortheoriginal,quadraticversionofDP-
SLAM versusthe linearimplementation. . . . . . . . . . . . . . . . . . . 60
10.1 Summary ofComputationalComplexity . . . . . . . . . . . . . . . . . . 105
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List of Figures
2.1 A plot of the robot’s actual motion, shown in grey, compared with the
trajectorydescribedbythe odometry,showninblack. . . . . . . . . . . . 7
3.1 Effectofangleonnumber ofgridcellspenetrated. . . . . . . . . . . . . . 21
3.2 Effect of grid resolution on scan probabilities. If the grid squares all have
the same density to the sensor, the scan on the left should have the same
probability astheoneonthe right. . . . . . . . . . . . . . . . . . . . . . 22
4.1 ASLAMalgorithminprogress,demonstratingthedistributionofparticles.
This illustrates the possibledifferencesbetweenthe mostlikely particleat
a given time step, and the true pose. This partial map corresponds to the
upperleft cornerofthe finalmapin Figure4.2. . . . . . . . . . . . . . . 28
4.2 The results of ignoring the joint distribution over maps and robot poses,
and maintaining only a single map. The two sections of hallway at the
bottom aresupposedtoline up. . . . . . . . . . . . . . . . . . . . . . . . 28
4.3 An ancestrytreejustbeginning . . . . . . . . . . . . . . . . . . . . . . . 33
4.4 Thechildren particlesare propagatedthroughtheparticlefilter. . . . . . . 33
4.5 Thechildparticles areresampledforthe nextgeneration . . . . . . . . . 34
4.6 Unnecessaryancestorparticles arepruned. . . . . . . . . . . . . . . . . . 34
4.7 Resamplingin thenextgeneration . . . . . . . . . . . . . . . . . . . . . 35
4.8 Irrelevant ancestorsare pruned,andthe columnon the leftis collapsed. . 35
4.9 SLAM usingasinglemap. . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.10 ADP-SLAMmap with9000particles. . . . . . . . . . . . . . . . . . . . 46
4.11 Deterministic occupancygridsfail tohandlethe difficultiesofC-Wing . . 47
4.12 Proper stochastic mapping can successfully close the loop in C-Wing, us-
ing thesamenumberofparticles. . . . . . . . . . . . . . . . . . . . . . . 48
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