Autonomous Flight in Unstructured and Unknown Indoor Environments Abraham Galton Bachrach

126 Pages·2009·3.66 MB·English

Autonomous Flight in Unstructured and Unknown Indoor
Environments
by
Abraham Galton Bachrach
Submitted to the Department of Electrical Engineering and Computer
Science
in partial fulfillment of the requirements for the degree of
Master of Science
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2009
(cid:13)c Massachusetts Institute of Technology 2009. All rights reserved.
Author .............................................................
Department of Electrical Engineering and Computer Science
September 4, 2009
Certified by .........................................................
Nicholas Roy
Associate Professor of Aeronautics and Astronautics
Thesis Supervisor
Accepted by.........................................................
Terry P. Orlando
Professor of Electrical Engineering and Computer Science
Chair, Department Committee on Graduate Students
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Autonomous Flight in Unstructured and Unknown Indoor
Environments
by
Abraham Galton Bachrach
SubmittedtotheDepartmentofElectrical EngineeringandComputerScience
on September4,2009, inpartialfulfillmentofthe
requirementsforthedegreeof
MasterofScience
Abstract
This thesis presents the design, implementation, and validation of a system that enables
a micro air vehicle to autonomously explore and map unstructured and unknown indoor
environments. Suchavehiclewouldbeofconsiderableuseinmanyreal-worldapplications
such as search and rescue, civil engineering inspection, and a host of military tasks where
it is dangerous or difficult to send people. While mapping and exploration capabilities
are common for ground vehicles today, air vehicles seeking to achieve these capabilities
face unique challenges. While there has been recent progress toward sensing, control,
and navigationsuitesfor GPS-denied flight, there have been few demonstrationsof stable,
goal-directed flightinreal environments.
The main focus of this research is the development of real-time state estimation tech-
niques that allow our quadrotor helicopter to fly autonomously in indoor, GPS-denied en-
vironments. Accomplishingthisfeatrequiredthedevelopmentofalargeintegratedsystem
that brought together many components into a cohesive package. As such, the primary
contribution is the development of the complete working system. I show experimental re-
sultsthatillustratetheMAV’sabilitytonavigateaccuratelyinunknownenvironments,and
demonstrate that our algorithms enable the MAV to operate autonomously in a variety of
indoorenvironments.
ThesisSupervisor: NicholasRoy
Title: AssociateProfessorofAeronauticsand Astronautics
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Acknowledgments
I would like to thank all of the people in my life who have gotten me here. Without your
patient support, guidance, friendship and love this thesis, and the work herein would not
havebeen possible. Specifically Iwouldliketothank:
• My advisor, ProfessorNicholas Roy forguidingmethrough thisprocess. Themany
discussions we have had over the years have helped clarify the concepts and ideas I
needed tolearn to makethiswork feasible.
• Ruijie He and Samuel Prentice my conspirators in everything quadrotor related. It
has been a lot of fun working with you, and together we have accomplished great
things.
• Markus Achtelik for his role in developing the vision system for the quadrotor, and
Garrett Hemannforfixing thevehicleswhen webreak them.
• Daniel Gurdan, Jan Stumpf, and the rest of the Ascending Technologies team for
creating suchimpressivevehiclesforus.
• AlbertHuangforhisassistancewiththetransitiontoLCM,aswellasthemanymore
general algorithmand systemdesigndiscussions.
• MattWalter,JohnRoberts,RickCory,OlivierKoch,TomKollar,AlexBahr,andthe
rest of the people here at CSAIL who have discussed and helped me work through
myquestions.
• EdwinOlsonforprovidingthereference codeforhisscan-matchingalgorithm.
• My wonderful friends in Boston, California, and elsewhere who help distract me
frommy workwhen needed tokeep mesane. Youimprovemy qualityoflife.
Last, but certainly not least, Rashmi, Devra, Lela, Bimla, Mom, Dad, and the rest of my
family for shaping me into the man I am today with your constant love, support, and en-
couragement!
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Contents
1 Introduction 15
1.1 KeyChallenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2 ProblemStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.5 SystemOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.6 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2 RelativePositionEstimation 29
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2 LaserScan-Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.1 IterativeClosest Point . . . . . . . . . . . . . . . . . . . . . . . . 32
2.2.2 Map Based ProbabilisticScan-Matching . . . . . . . . . . . . . . . 34
2.2.3 Robust High-SpeedProbabilisticScan-Matching . . . . . . . . . . 36
2.2.4 Laser Based HeightEstimation . . . . . . . . . . . . . . . . . . . . 46
2.3 VisualOdometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.3.1 Feature detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.3.2 Feature Correspondence . . . . . . . . . . . . . . . . . . . . . . . 52
2.3.3 Frame toframemotionestimation . . . . . . . . . . . . . . . . . . 53
2.3.4 Nonlinearmotionoptimization . . . . . . . . . . . . . . . . . . . . 53
2.4 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
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3 System Modules 57
3.1 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2 DataFusionFilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.2.1 Extended KalmanFilterBackground . . . . . . . . . . . . . . . . . 59
3.2.2 Process Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.2.3 Measurement Model . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.2.4 FilterTuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.3 PositionControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4 SLAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.5 Planningand Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.6 ObstacleAvoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4 Experiments 79
4.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.1.1 Laser Configuration . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.1.2 Stereo Configuration . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.1.3 CombinedConfiguration . . . . . . . . . . . . . . . . . . . . . . . 83
4.2 FlightTests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.2.1 Laser Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.2.2 Stereo Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.2.3 Laser andStereo . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5 Dense 3D Environment Modeling 93
5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.2 OurApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.2.1 TriangularSegmentation . . . . . . . . . . . . . . . . . . . . . . . 97
5.2.2 DataAssociation . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.2.3 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6 Object Tracking 103
6.1 LearningObject AppearanceModels . . . . . . . . . . . . . . . . . . . . . 104
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6.2 ImageSpace Object Tracking . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.3 TrackingAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
6.4 Person Following . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
7 Conclusion 115
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
7.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
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