Trajectory Bundle Estimation For Perception-Driven Planning Abraham Galton Bachrach

122 Pages·2010·7.9 MB·English

by  Abraham Galton Bachrach

Trajectory Bundle Estimation For Perception-Driven
Planning
by
Abraham Galton Bachrach
Submitted to the Department of Electrical Engineering and Computer
Science
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
February 2013
(cid:13)c Massachusetts Institute of Technology 2013. All rights reserved.
Author .............................................................
Department of Electrical Engineering and Computer Science
December 31, 2012
Certified by .........................................................
Nicholas Roy
Professor of Aeronautics and Astronautics
Thesis Supervisor
Accepted by.........................................................
Leslie Kolodziejski
Professor of Electrical Engineering and Computer Science
Chair, Department Committee on Graduate Students
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Trajectory Bundle Estimation For Perception-Driven Planning
by
Abraham Galton Bachrach
SubmittedtotheDepartmentofElectricalEngineeringandComputerScience
onDecember31,2012,inpartialfulfillmentofthe
requirementsforthedegreeof
DoctorofPhilosophy
Abstract
When operating in unknown environments, autonomous vehicles must perceive and un-
derstand the environment ahead in order to make effective navigation decisions. Long
rangeperceptioncanenableavehicletochooseactionsthattakeitdirectlytowarditsgoal,
avoiding dead ends. In addition, the perception range is critically important for ensuring
thesafetyofvehicleswithconstraineddynamics. Ingeneral,thefasteravehiclemoves,the
more constrained its dynamics become due to acceleration limits imposed by its actuators.
Thismeansthatthespeedatwhichanautonomousagentcansafelytravelisoftengoverned
by its ability to perceive and understand the environment ahead. Overall, perception range
is one of the most important factors that determines the performance of an autonomous
vehicle.
Today, autonomous vehicles tend to rely exclusively on metric representations built
using range sensors to plan paths. However, such sensors are limited by their maximum
range, field of view, and occluding obstacles in the foreground. Together, these limitations
make up what we call the metric sensing horizon of the vehicle. The first two limitations
are generally determined by the weight, size, power, and cost budget allocated to sensing.
However,rangesensorswillalwaysbelimitedbyocclusions.
If we wish to develop autonomous vehicles that are able to navigate directly toward a
goal at high speeds through unknown environments, then we must move beyond the sim-
ple range-sensor based techniques. We must develop algorithms that enable autonomous
agents to harness knowledge about the structure of the world to interpret additional sensor
information (such as appearance information provided by cameras), and make inferences
aboutpartsoftheworldthatcannotbedirectlyobserved.
Wedevelopanewrepresentationbasedaroundtrajectorybundles,thatmakesthischal-
lenging task more tractable. Rather than attempt to explicitly model the geometry of the
worldinfrontofthevehicle(whichcanbeincrediblycomplex),wereasonabouttheworld
intermsofwhatthevehiclecanandcannotdo. Trajectorybundlesaredesignedtocapture
an abstract concept such as the command “go straight and then turn towards the right” in
a concrete and actionable manner. We employ a library of trajectory bundles to reason
about the layout of obstacles in the environment based on which bundles in the library are
predicted to be feasible. Trajectory bundles provide a lens through which we can look at
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perception tasks, allowing us to leverage machine learning tools in much more effective
waysfornavigation.
In this thesis we introduce trajectory bundles, and develop algorithms that use them
to enable perception-driven planning. We develop a trajectory clustering algorithm that
enables us to construct a set of trajectory bundles. We then develop a Bayesian filtering
framework that enables us to estimate a belief over which trajectory bundles are feasible
based on the history of actions and observations of the vehicle. We test our algorithms
by using them to navigate a simulated fixed wing air vehicle at high speeds through an
unknownenvironmentusingamonocularcamerasensor.
ThesisSupervisor: NicholasRoy
Title: ProfessorofAeronauticsandAstronautics
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Acknowledgments
Ihavelearnedsomuch,andhadsomuchfuntheselastseveralyears. Iamextremelylucky
tohavehadsuchanamazingopportunity. Iwouldliketothankthemanypeoplewhohave
helped me get here, and who have made the experience what it was. Without your patient
support,guidance,friendshipandloveIwouldnotbewhereIamtoday.
In particular, I would like to thank my advisor, professor Nicholas Roy for his support
and guidance. He has been a great sounding board for ideas, and instrumental in molding
my loose ramblings into coherent research. He has in all ways exemplified what it means
tobeamentor.
MythesiscommitteeconsistingofSethTeller,andTomasLozano-Perezwhohavebeen
invaluable resources, and helped refine both the thesis problem, as well as the technical
approach. In addition, I would like to thank professors Russ Tedrake, John Leonard, Bill
Freeman, and Antonio Torralba for always being happy to discuss my research, and give
metheirtakeonthings.
I would also like to thank the many friends and collaborators who I have worked with.
Samuel Prentice and Ruijie He, my conspirators in everything quadrotor related. Adam
Bry for bringing a host of new challenges and ideas to make our research more dynamic
and exciting. John Roberts for always being willing to debate anything and everything.
Ourmanydartgameswereimportantrelaxation,yetalsofosteredmanyimportantresearch
discussions. AlbertHuangfortheusefulguidance,instruction,andalwaysbeingwillingto
show me how to do things “the right way”. Thanks to Charles Richter, Will Vega-Brown,
and Johnathan Kelly for continuing on with the air vehicle research. I cannot wait to see
wherethingsprogress.
I also benefited tremendously from all the discussions about anything and everything
with everyone else in 33x and elsewhere in CSAIL. In particular, I’d like to thank (in
alphabetical order) Alex Bahr, Marec Doniec, Maurice Fallon, Hordur Johannsson, Josh
Joseph,MichaelKaess,SertacKaraman,RossKnepper,OlivierKoch,TomKollar,Stefanie
Tellex,FinaleDoshi-Velez,JavierVelez,andMattWalter. Thankyouforbeingsogenerous
withyourtime.
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In addition to the people more directly involved in my academic pursuits, I would like
to thank my friends in Boston, California, and elsewhere who helped distract me from my
work when needed to keep me sane. In particular Katharine Wolf, for your constant care,
affection, support, and also for always helping me see that the glass was half full (and this
thesiswashalfdone;-)
Finally, I am grateful to my sisters Rashmi, Devra, Lela, and Bimla for always encour-
aging me, and being such great role models for me to try to live up to. Last, but certainly
not least, I would like to thank my parents for always supporting me, while at the same
timepushingmetoexcel.
Thankyouallsomuch!
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Contents
1 Introduction 13
1.1 Perception-DrivenPlanning . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2 TrajectoryBundles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.3 EstimationofTrajectoryBundleFeasibility . . . . . . . . . . . . . . . . . 26
1.4 ThesisGoal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.5 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.6 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.7 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2 RelatedWork 31
2.1 EnvironmentRepresentations . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.1.1 PointClouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.1.2 OccupancyMaps . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.1.3 PolygonalObstacleMaps . . . . . . . . . . . . . . . . . . . . . . 35
2.1.4 TopologicalMaps . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.2 PlanningThroughUnknownEnvironments . . . . . . . . . . . . . . . . . 37
2.2.1 OccupancyMapBasedNavigation . . . . . . . . . . . . . . . . . . 38
2.2.2 OpticalFlowBasedObstacleAvoidance . . . . . . . . . . . . . . . 39
2.2.3 MappingSensingToActions . . . . . . . . . . . . . . . . . . . . . 40
2.2.4 DepthFromaMonocularImage . . . . . . . . . . . . . . . . . . . 42
2.2.5 TrajectoryLibraries . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.2.6 TrajectoryPrediction . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2.7 DARPALearningAppliedtoGroundRobotsProgram . . . . . . . 47
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3 TrajectoryLibraryBasedPlanning 49
3.1 SimulationModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.1.1 VehicleModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.1.2 SensorModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.1.3 EnvironmentModel . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2 BaselinePlanningSystem . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.2.1 TrajectoryLibraryGeneration . . . . . . . . . . . . . . . . . . . . 53
3.2.2 Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2.3 Backtracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3 BaselinePlanningPerformance . . . . . . . . . . . . . . . . . . . . . . . . 56
4 TrajectoryBundleLibraryGeneration 61
4.1 TrajectorySimilarityMetrics . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.1.1 DynamicTimeWarping . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 AffinityPropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 ClusteringAlgorithmEvaluation . . . . . . . . . . . . . . . . . . . . . . . 67
4.4 TrajectoryBundleLibraryGeneration . . . . . . . . . . . . . . . . . . . . 69
5 TrajectoryBundlePrediction 71
5.1 NearestNeighborPrediction . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.1.1 ClassificationPerformance . . . . . . . . . . . . . . . . . . . . . . 75
5.1.2 OccupancyMapPrediction . . . . . . . . . . . . . . . . . . . . . . 77
5.2 SpatialConditionalRandomFieldModel . . . . . . . . . . . . . . . . . . 79
5.2.1 Independent-NodeShared-FeatureModel . . . . . . . . . . . . . . 82
5.2.2 Chow-LiuTreeModel . . . . . . . . . . . . . . . . . . . . . . . . 83
5.2.3 CRFPredictionPerformance . . . . . . . . . . . . . . . . . . . . . 84
5.3 PlanningPerformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6 BayesianFilteringInTheSpaceofTrajectoryBundles 89
6.1 HiddenMarkovModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.2 DynamicBayesNetworkModels . . . . . . . . . . . . . . . . . . . . . . . 92
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6.3 BayesFilterModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.4 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.5 PredictionAccuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.6 PlanningPerformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.6.1 CombiningPredictionswithRangeSensing . . . . . . . . . . . . . 99
7 ExperimentalAnalysis 101
7.1 DataCollection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
8 Conclusion 107
8.1 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
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