Performance of POSIT forreal-time UAV pose estimation

Chayatat RatanasawanyaMarch 16, 2011

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OverviewThesis problemThe UAVPose estimation by POSIT

Previous workDevelopment of POSIT-based real-time pose

estimation algorithmExperimental resultsQuestions

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Thesis problem statementDevelop a flexible human/machine control

system to hover an UAV carrying a VDO camera beside an object of interest such as a window for surveillance purposes.

Method: Human control – Joystick Machine control – Visual-servoing

Application: for the police to use the system to survey a room from outside of a building.

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The UAVQ-ball: 6DOF quadrotor helicopterCame with SIMULINK-based

real-time controllersy

x

z

World frame

Helicopter

ControllerX, Z

(desired)

OptitrackX, Z

IMU Roll,

Pitch

SonarY

Y (desired)Yaw(desired)

YawMagnetometer

Desired inputs

X, Y, Z, Yaw

Camera

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POSIT algorithmDevelopers: Daniel DeMenthon & Philip

DavidThe algorithm determines the pose of an

object relative to the camera from a set of 2D image points

Reference: http://www.cfar.umd.edu/~daniel/classicPosit.m

POSIT

Image coordinates of min. 4 non-coplanar

feature points3D object

coordinates of the same points

Camera intrinsic

parameters (f, cc)

Rotation matrix of object wrt.

cameraTranslation of

object wrt. camera

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Previous workCardboard box targetTook still images of the target from

various locations in the labManual feature points identificationObject pose was estimated offlineTarget was self-occludedNot a real-time process

y

x

z

Object frame

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Current workImage-based control algorithm is being

developed

Must be a real-time processUAV pose must be estimated real-time

Target must not be self-occluded Image source: Live video Image processing has to be fast Feature points must be identified automatically

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Feature points extraction

Camera

Detect LED

Detect Window

Detect Corners

Discard unwanted

feature points detected

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Distortion coeff. from cam

calibration

Feature points undistortion?Fast image processing – no unnecessary

calculationsEvaluate the pose estimated by POSIT from

distorted and undistorted feature points locations

VDO from Camera

Feature points

extraction

Undistortion by look-up

table

POSIT & Inv.

kinematics

Points location

filter

Compare

Optitrack

IMU

POSIT & Inv.

kinematics6DOF UAV pose

estimates

6DOF UAV pose

RollPitch

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Experimental setup

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Experimental setupThe Q-ball was randomly placed in 20

locations in the lab. Its pose was different in each location.

Acquire live video stream and estimate the UAV pose with POSIT in real-time.

150 6DOF pose estimations, Optitrack, and IMU readings were recorded.

Optitrack readings are used as reference.

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Results - XTest Undistorted

pointsDistorted

points Optitrack

1 1.006 1.1748 0.0012 15.8091 14.7321 0.00143 101.816 90.9279 0.00574 3.0482 3.5745 0.00135 2.0852 2.2731 0.00156 5.8156 5.688 0.00167 1.5017 1.4547 0.00148 5.2479 5.4141 0.00119 4.1441 4.4484 0.0015

10 1.2467 1.2847 0.00311 9.0607 9.9454 0.004612 5.5611 6.6607 0.006713 4.3397 4.0936 0.001414 3.5994 3.782 0.001615 38.0258 54.0048 0.001716 2.1597 2.2568 0.001317 8.0223 4.3796 0.001418 0.7739 0.8675 0.001319 0.9564 1.0413 0.001520 7.2199 8.0107 0.0016

Standard Deviation

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Results - YTest Undistorted

pointsDistorted

points Optitrack

1 1.0235 1.3126 0.00162 3.2475 6.0226 0.00163 65.5548 53.7607 0.00154 12.6819 12.8114 0.00135 2.3903 2.5973 0.00156 6.0252 4.0652 0.00137 0.2603 0.313 0.00138 0.9041 0.8921 0.00139 1.419 1.515 0.0016

10 1.6733 1.7693 0.015611 5.2706 5.9692 0.002312 5.2413 6.273 0.001413 2.1456 2.3075 0.003314 0.76 0.8236 0.001115 24.5723 34.6469 0.001716 1.3318 1.5206 0.001417 0.7696 0.4422 0.001318 1.5774 1.8228 0.001419 0.921 0.9587 0.001720 4.578 3.3981 0.0013

Standard Deviation

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Results - Z Standard Deviation

Test Undistorted points

Distorted points Optitrack

1 0.3511 0.4984 0.00072 2.1178 5.2621 0.00123 30.399 43.4517 0.00124 3.8319 3.7698 0.00095 1.0314 1.0843 0.00116 2.078 1.6947 0.00097 0.1703 0.1464 0.00128 0.429 0.4984 0.0019 1.9335 1.9616 0.0007

10 0.8854 0.9199 0.001411 2.2709 1.9648 0.001112 1.671 1.7368 0.001313 0.5539 0.7699 0.001214 1.4632 1.4751 0.000815 21.0649 35.0081 0.00116 0.4632 0.5104 0.000917 3.1985 1.6658 0.000818 0.6383 0.7831 0.001119 0.8268 0.9394 0.001120 1.2445 1.3881 0.0012

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Results - Roll Standard DeviationTest Undistorted points

Distorted points Optitrack IMU

1 0.1612 0.1823 0.002 0.17522 0.4117 0.1993 0.0018 0.18113 20.6804 20.4654 0.0026 0.13424 0.3264 0.3303 0.0014 0.13775 0.3735 0.3757 0.0018 0.13666 0.2521 0.2036 0.0018 0.14687 0.1268 0.1168 0.0011 0.13348 0.1193 0.1243 0.0014 0.14489 0.2411 0.2424 0.0018 0.1162

10 0.1656 0.174 0.0323 0.14611 0.3348 0.3453 0.0035 0.13312 0.3425 0.3959 0.0015 0.142113 0.3584 0.3624 0.004 0.147914 0.3513 0.3511 0.001 0.141715 32.7365 49.532 0.0024 0.150216 0.1705 0.172 0.0014 0.149717 0.3527 0.1812 0.0011 0.153518 0.1606 0.1734 0.0021 0.15819 0.2017 0.2068 0.0028 0.154120 0.1617 0.1547 0.0014 0.1755

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Results - Pitch Standard DeviationTest Undistorted points

Distorted points Optitrack IMU

1 0.2929 0.341 0.0024 0.17132 0.6308 1.3458 0.0024 0.18993 30.0484 37.8968 0.0024 0.17124 4.1615 4.0302 0.0019 0.18355 0.6895 0.7135 0.0021 0.17616 1.3932 0.8914 0.0023 0.15277 0.1101 0.1174 0.0018 0.16318 0.108 0.113 0.0021 0.1869 0.3816 0.3925 0.0026 0.1635

10 0.4045 0.4179 0.0263 0.164111 1.0534 1.112 0.0041 0.196912 1.023 1.117 0.0018 0.1613 0.5073 0.5361 0.0057 0.196614 0.2661 0.2695 0.0018 0.171215 24.6641 37.3222 0.0029 0.152816 0.4542 0.4817 0.0018 0.166417 0.1648 0.0821 0.0018 0.160918 0.3886 0.4266 0.0035 0.1519 0.3142 0.3175 0.0027 0.19720 0.8514 0.6 0.002 0.1685

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Results - Yaw Standard Deviation

Test Undistorted points

Distorted points Optitrack

1 0.2562 0.2708 0.00142 2.313 1.8 0.00173 6.73 7.9832 0.00684 0.8439 1.009 0.00115 0.5683 0.604 0.00126 1.3341 1.2284 0.00117 0.5728 0.5252 0.00218 1.2824 1.2913 0.00129 0.9123 0.9474 0.001

10 0.2592 0.2629 0.004911 1.7751 1.8515 0.002712 1.0973 1.2381 0.006913 0.9673 0.8913 0.001314 0.9592 0.9743 0.001215 5.4126 12.7222 0.001716 0.6324 0.6399 0.000917 1.9195 0.9885 0.00118 0.2138 0.2222 0.001519 0.3118 0.3278 0.000920 1.3624 1.4042 0.0011

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Mean and SD of errorof all 3000 measurements

DOFDistorted feature points

w.r.t. to OptitrackUndistorted feature

points w.r.t. to Optitrack Optitrack w.r.t. IMU

Mean SD Mean SD Mean SDX (cm) 13.9990 10.3494 16.5993 10.9070 N/A N/AY (cm) 3.9571 4.4958 3.3956 3.8116 N/A N/AZ (cm) 17.5677 8.1526 5.7379 4.3130 N/A N/ARoll (⁰) 1.6193 1.3389 1.2874 1.1580 1.3289 1.0149

Pitch (⁰) 1.3662 1.3103 1.5493 1.4160 0.7635 0.3610Yaw (⁰) 3.5570 1.8350 4.1395 1.8739 N/A N/A

DOFDistorted feature points

w.r.t. to OptitrackUndistorted feature

points w.r.t. to Optitrack Optitrack w.r.t. IMU

Mean SD Mean SD Mean SDX (cm) 15.7600 25.5053 17.6538 25.3427 N/A N/AY (cm) 5.7741 15.4753 4.6884 16.5980 N/A N/AZ (cm) 18.2766 12.6632 6.1423 9.1493 N/A N/ARoll (⁰) 3.0873 13.0284 2.0754 9.0004 1.4342 1.1588

Pitch (⁰) 2.9703 12.4040 2.4043 8.7745 0.7679 0.3615Yaw (⁰) 3.6342 3.6540 4.0707 2.4474 N/A N/A

Excludes #3 &

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ConclusionPOSIT algorithm is an alternative for real-

time UAV pose estimationTarget consists of a white LED and a window5 non-coplanar feature pts: the LED and 4

cornersPose estimation using undistorted feature

points is more accurate than that using distorted points – significant improvement along Z-direction

Image information may be mapped to positional control inputs via POSIT algorithm

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SummaryThesis problem & the UAVPrevious work on POSIT – the drawbacksPOSIT-based real-time pose estimation

algorithmFeature points extraction from live VDOFeature points image coordinates undistortionFeature points location filteringReal-time algorithm

Comparison between pose estimated by POSIT, pose from Optitrack, and 2 attitude angles from IMU.