Inertial and Magnetic Angle Tracking of Human Limb Segments for Inserting Humans into Synthetic ...

197 Pages·2001·1.44 MB·English

NAVAL POSTGRADUATE SCHOOL
Monterey, California
DISSERTATION
INERTIAL ANDMAGNETICTRACKING OFLIMB
SEGMENT ORIENTATIONFORINSERTING HUMANS
INTO SYNTHETICENVIRONMENTS
by
Eric Robert Bachmann
December2000
DissertationSupervisor: Michael J.Zyda
Approvedforpublicrelease; distributionis unlimited.
Approvedforpublic release; distributionis unlimited
INERTIAL ANDMAGNETICANGLETRACKING OFLIMB SEGMENTS
FORINSERTINGHUMANS INTO SYNTHETICENVIRONMENTS
by
EricRobertBachmann
B.A.,Universityof Cincinnati,1983
M.S.,NavalPostgraduate School,1995
Submittedinpartial fulfillmentof the
requirements forthedegree of
DOCTOROFPHILOSOPHYINCOMPUTERSCIENCE
fromthe
NAVAL POSTGRADUATE SCHOOL
December 2000
Author:
_______________________________________________
EricR.Bachmann
Approvedby:
________________________________________________
Michael J.Zyda,Professorof ComputerScience
___________________________________ __________________________________
Robert B.McGhee XiaopingYun,
ProfessorofComputerScience ProfessorofElectricalEngineering
___________________________________ __________________________________
Man-TakShing,Associate DonaldP.Brutzman,Assistant
ProfessorofComputerScience ProfessorofAppliedScience
Approvedby:
_______________________________________________
Daniel Boger,Chair,Department ofComputerScience
Approvedby:
_______________________________________________
AnthonyCiavarelli, Associate Provost ofInstruction
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ABSTRACT
Current motion tracking technologies fail to provide accurate wide area tracking of
multiple users without interference and occlusion problems. These limitations make
difficult the construction of a practical and intuitive interface, which allows humans to be
insertedintonetworkedvirtualenvironmentsinafullyimmersivemanner.Advancesinthe
field of miniature sensors make possible inertial/magnetic tracking of human body limb
segment orientation without the limitations of current systems. Due to implementation
challenges, inertial/magnetic sensors have not previously been used successfully for full
bodymotioncapture.Thisresearchproposestoovercomethesechallengesusingmulti-axis
sensors combined with a quaternion-based complementary filter algorithm capable of
continuously correcting for drift and following motion through all orientations without
singularities.
Primarily, this research involves the development of a prototype tracking system to
demonstratethefeasibilityofhybridRF/magnetic/inertialmotiontracking.Constructionof
inertial/magnetic (MARG) sensors is completed using off-the-shelf components.
Mathematical analysis and computer simulation are usedto validate the correctness of the
complementary filter algorithm. The implemented human body model utilizes the world-
coordinate reference frame orientation data provided in quaternion form by the
complementary filter and orients each limb segment independently. Calibration of the
model and the inertial sensors is accomplished using simple but effective algorithms.
Physical experiments demonstrate the utility of the proposed system. These experiments
involve the trackingofhumanlimbs inreal-timeusingmultipleinertial sensors.
The motion tracking system produced has an accuracy which is comparable and a
latency which is superior to active electro-magnetic sensors. The system is “sourceless”
and does not suffer from range restrictions and interference problems. With additional
MARG sensor packages, the architecture produced will easily scale to full body tracking.
This new technology overcomes the limitations of motion tracking technologies currently
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in use. It will provide wide area tracking of multiple users in virtual environment and
augmentedrealityapplications.
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TABLE OF CONTENTS
I. INTRODUCTION .................................................................................................... 1
A. MOTIVATION................................................................................................ 1
B. GOALS ............................................................................................................5
1. Problemtobe Solved .............................................................................5
2. What is FundamentallyNew ..................................................................5
3. Contributionofthis Research ................................................................6
C. METHOD ........................................................................................................6
D. DISSERTATIONORGANIZATION .............................................................7
II. SURVEYOFTRACKING TECHNOLOGIES ....................................................... 9
A. INTRODUCTION........................................................................................... 9
B. MOTIONTRACKINGTECHNOLOGIES ....................................................9
a.FrameworkforSuitability ..................................................................9
b.PerformanceRequirements ..............................................................11
1. Mechanical Trackers ............................................................................13
2. MagneticTrackers ................................................................................15
3. Optical Sensing ....................................................................................19
a.PatternRecognitionSystems ...........................................................20
b.ImageBasedSystems ......................................................................21
c.StructuredLight andLaserSystems ................................................24
4. AcousticTrackers ................................................................................25
5. InertialandMagneticTracking ............................................................25
6. RF Positioning ......................................................................................28
7. HybridTrackingSystems .....................................................................29
8. OtherTechnologies ..............................................................................31
C. SUMMARY ...................................................................................................32
III. REPRESENTATIONOFHUMAN BODYMOTIONANDMODELING ......... 33
A. INTRODUCTION......................................................................................... 33
B. RIGIDBODYORIENTATION REPRESENTATION ................................33
1. EulerAngles .........................................................................................34
a.Euler Angle Rotation .......................................................................35
b.TransformingBodyRates ToEulerRates .......................................36
c.Euler Angle Singularities .................................................................38
2. Quaternions ..........................................................................................39
a.QuaternionOperations .....................................................................40
b.QuaternionForms ............................................................................41
c.QuaternionTransformationBetweenCoordinateFrames ...............42
d.UnitQuaternions InPositiveRealForm ..........................................44
e.TransformingAngularRates ToAQuaternionRate .......................44
f.RepresentingOrientations Without Singularities .............................46
C. MODELS FOR HUMANBODY TRACKING ............................................46
1. KinematicModels BasedOnHomogenous TransformationMatrices 47
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2. ForwardandInverse Kinematics .........................................................48
3. KinematicModels oftheHumanBodybasedonJoint Angles ...........49
4. OrientationOnlyTracking ...................................................................51
5. KinematicModels basedonQuaternion/VectorPairs ........................51
D. SUMMARYAND CONCLUSIONS ............................................................55
IV. REVIEWOF FILTER THEORYANDDESIGN ................................................ 57
A. INTRODUCTION......................................................................................... 57
B. MINIATUREINERTIALSENSORS ...........................................................58
C. RANDOM PROCESSES ..............................................................................60
D. LEASTSQUARES FILTERING ..................................................................62
E. WIENERFILTERING ..................................................................................64
1. Continuous WeinerFilters ...................................................................65
2. Discrete WeinerFilters ........................................................................67
F. KALMANFILTERING ................................................................................68
1. Discrete KalmanFilters .......................................................................69
2. ExtendedandLinearizedKalmanFilters .............................................71
G. COMPLEMENTARYFILTERING ..............................................................73
1. CrossoverFrequency ............................................................................76
H. SUMMARYAND CONCLUSIONS ............................................................77
V. AQUATERNIONATTITUDEFILTER .............................................................. 81
A. INTRODUCTION......................................................................................... 81
B. AQUATERNIONATTITUDEFILTER ......................................................81
1. ParameterOptimization .......................................................................83
2. Analysis ................................................................................................86
a.NoiseResponse ................................................................................87
b.Response toInitialConditionErrors ...............................................87
c.ChoosingtheFeedbackgainvalue ...................................................89
3. ReducedOrderFilter ............................................................................91
a.OrthogonalQuaternionTheorem .....................................................92
4. Differential WeightingofSensorData ................................................95
5. ReducedRate Drift Correction ............................................................96
C. FILTERSIMULATION ................................................................................97
D. SUMMARY ...................................................................................................98
VI. IMPLEMENTATION OFINERTIALANDMAGNETIC TRACKINGOF
HUMANLIMB SEGMENTS ............................................................................. 101
A. INTRODUCTION....................................................................................... 101
B. PROTOTYPEMARGSENSORS ...............................................................102
1. Sensor Components ............................................................................104
a.CrossbowCXL04M3Triaxial Accelerometer ...............................104
b.TokinCG-16D Series RateGyros .................................................104
c.Honeywell HMC20033-Axis Magnetometer ................................105
2. Magnetometer Set/Reset ....................................................................106
3. AnalogtoDigitalConversion ............................................................107
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4. DataProcessing ..................................................................................107
C. SYSTEM SOFTWARE ...............................................................................108
1. QuaternionFilter ................................................................................113
2. Sensor Calibration ..............................................................................115
3. QuaternionHumanBodyModel ........................................................121
a.SettingModel PositionandPosture ...............................................123
b.BodyModel Calibration ................................................................126
D. SUMMARY .................................................................................................130
VII. EXPERIMENTALRESULTS ............................................................................. 131
A. INTRODUCTION....................................................................................... 131
B. STATIC STABILITY ..................................................................................131
C. STATIC CONVERGENCE ........................................................................135
D. DYNAMIC RESPONSEANDACCURACY ............................................138
E. QUALITATIVETESTING .........................................................................138
1. WeightedLeast Squares .....................................................................138
2. Posture Estimation .............................................................................139
3. ReducedRate Drift Correction ..........................................................141
F. INTERSENSEINERTIACUBE ..................................................................143
G. SUMMARY .................................................................................................145
VIII. SUMMARYAND CONCLUSIONS ................................................................... 147
A. INTRODUCTION....................................................................................... 147
B. MARG SENSORS .......................................................................................147
C. HUMANBODYMODELING....................................................................150
D. INTERGRATION OFINERTIALAND RF TECHNOLOGIES ...............152
E. WIRELESSCOMMUNICATIONS ............................................................153
F. FILTERING .................................................................................................154
G. APROTOTYPEINERTIALTRACKINGBODY SUIT ...........................155
H. POSTUREDATA INANETWORKEDSYNTHETIC
ENVIRONMENT ........................................................................................156
I. CONCLUSIONS .........................................................................................157
APPENDIXA.DERIVATIONOF GAUSS-NEWTONITERATION EQUATIONS 159
APPENDIXB.DERIVATION OFTHEXMATRIX ................................................. 161
APPENDIXC.VIDEODEMONSTRATION ............................................................. 165
LISTOFREFERENCES ................................................................................................167
INITIALDISTRIBUTION LIST ...................................................................................175
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LIST OF FIGURES
Figure 1:Exoskeletontrackingoftheupperbody ..........................................................14
Figure 2:ElectromagneticOrientationOnlyTrackingof the HumanBody
From[Ref.78.] ..............................................................................................18
Figure 3:Frame Assignment UnderMDHAfter[Ref.17.] ...........................................48
Figure 4:Inertial MotionTrackingofthe Right ForeandUpperArm withTwoInertial
Sensors andaQuaternionAttitude FilterFrom [Ref.88.] ............................52
Figure 5:HumanModel DesignedForQuaternionInput ...............................................53
Figure 6:BlockDiagrams ofLinearSystems .................................................................62
Figure 7:KalmanFilterLoopAfter[Ref.14.] ...............................................................71
Figure 8:ComplementaryFilterBlockDiagram ............................................................74
Figure 9:Transform DomainBlockDiagram OfRollAngle EstimationFilter .............75
Figure 10:Quaternion-BasedAttitudeFilter From[Ref.8.] ..........................................82
Figure 11:Signal FlowGraphfor LinearizedSystemAfter[Ref.54.] ..........................86
Figure 12:SimplifiedSFGFor StaticTestingWithZeroNoise After [Ref.55.] ..........87
Figure 13:Transform DomainSFG ForAfter[Ref.55.] ...............................................88
Figure 14:BlockDiagram OfTimeDomainLinearizedQuaternionAttitudeFilter .....89
Figure 15:SimulatedNonlinearFilterResponse,10Degree Offset,a=0.1,Dt=0.1From
[Ref.6.] ..........................................................................................................98
Figure 16:PrototypeInertial andMagneticBodyTrackingSystem ............................102
Figure 17:PrototypeMARGSensorFrom[Ref.61.] ..................................................103
Figure 18:MARGSensorMagnetometerSet/Reset CircuitSchematic From[Ref.61.] ...
108
Figure 19:BodyTrackingSoftware SimplifiedClass Diagram ...................................110
Figure 20:Class InstanceData FlowDiagram .............................................................112
Figure 21:OrientationEstimationFlowChart .............................................................114
Figure 22:DialogForManuallySettingFilterParametersandSensorDataNullVoltages
andScaleFactors .........................................................................................115
Figure 23:RotatingSensor 90Degrees About Positivex-axis ForRateCalibration ...118
Figure 24:ConsoleDisplayOfSensorCalibrationResults .........................................121
Figure 25:WireframeRenderingOfTheQuaternion-BasedHumanModel ...............122
Figure 26:HumanModel Settings Dialog ....................................................................123
Figure 27:CalculationOf LimbSegment Positions .....................................................125
Figure 28:The setPosture MethodOftheCHumanModel Class .................................126
Figure 29:The renderFigureMethodOftheCHumanModelClass .............................127
Figure 30:BodyModel CalibrationReference Position ..............................................129
Figure 31:One HourStaticTest OfOrientationEstimateStability,k= 1.0,=1.0 ......132
Figure 32:15MinuteStatic Test OfOrientationEstimateStability,
NoMagnetometer Input,k=1.0 .................................................................133
Figure 33:15MinuteStatic Test OfOrientationEstimateStability,
NoAccelerometerInput,k=1.0 .................................................................133
Figure 34:60MinuteStatic Test OfOrientationEstimateStability,
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