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Visual Tracking on an Autonomous Self-contained
Humanoid Robot
Mauro Rodrigues, Filipe Silva, Vítor Santos
University of Aveiro
CLAWAR 2008
Eleventh International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines
08 – 10 September 2008, Coimbra, Portugal
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11th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines
University of Aveiro
Outline
OverviewObjectivesSelf-Contained PlatformVision SystemExperimental ResultsConclusions
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University of Aveiro
OverviewHumanoid Platform
Humanoid Robot developed at University of Aveiro
Ambition is participation at Robocup
Platform composed of 22 DOF’s Head on a PTU
arrangement
Up to 70 cm height and a mass of 6,5 kg
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University of Aveiro
OverviewDistributed Control Architecture
Master/Multi-Slave configuration on CAN Bus Central Processing Unit:
Image processing and visual tracking External computer interaction for
monitorization, debug or tele-operation
Master CPU/Slaves communication interface
Slaves Interface with actuators and sensors
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Objectives
Central Processing Unit Integration Computational autonomy Development environment
Vision System Development
Visual Tracking Approach Detection and tracking of a moving target (ball)
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Self-Contained Platform
CPU standard PCI-104 AMD Geode LX-800 @ 500MHz 512Mb RAM SSD 1Gb
Video Signal Capture PCMCIA FireWire board Dual PCMCIA PC104 module UniBrain Fire-i @ 30fps (640x480)
Camera
Development Environment Linux based OpenCV
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Vision System
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Vision SystemAcquisition
Mask
Segmentation - H, S and V Components
Object LocationPre-processing
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Vision System
Dynamic Region of Interest (ROI) Reduced noise impact Faster calculus
With ROINo ROI
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Vision SystemDynamic Region of Interest (ROI)
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University of Aveiro
Vision SystemVisual Tracking Approach
Keep target close to image centre Image based algorithm
Fixed gains proportional law, , joint increment vector , constant gain matrix , error vector defined by the ball’s offset
Variable gains nonlinear law,
Keq q
Tyx CCe ,
t
yt
t
xp
q
CK
q
CKq
cos,
sin
tp KKK ,
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Experimental ResultsSelf-Contained Platform
Acquisition libdc1394 based library
Acquisition @ 320x240 with down-sampling: ~24ms
Processing Without dynamic ROI: 15ms With dynamic ROI: 11ms
Total = ~40ms
25Hz
Times (ms)
Max. (ms) Min. (ms) Avg. (ms) St. Dev.
acquisition 32,4020 11,8780 13,6820 2,0275
pyr down 25,9050 9,4730 9,8432 1,6330
segmentation 41,6030 9,3320 9,8456 2,4185
centroid location 3,2550 0,3970 1,3079 0,4478
control 0,1590 0,0140 0,0154 0,0093
actuation 37,2460 2,1670 4,4850 2,6913
total 118,6600 35,9060 39,1520 7,1468
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Experimental ResultsVisual Tracking
Ball alignment ~1s Stationary error (~7 pixels)
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Experimental ResultsVisual Tracking
Pan tracking with fixed gains Error increases in frontal area of the robot
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Experimental ResultsVisual Tracking
Pan tracking with variable gains Frontal area error reduced
Fixed Gains Variable Gains
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Experimental ResultsVisual Tracking
Tilt tracking with variable gains Error similar to the pan tracking Trunk increases the error
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Experimental ResultsVisual Tracking
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Conclusions Implemented architecture separates the high-level vision
processing from the low-level actuators control Dynamic Region of Interest guarantees a greater noise
immunity and faster calculus Low error location and alignment with stationary target,
fast convergence Tracking error reveals the need of a more sophisticated
control
Autonomous Self-Contained Humanoid Platform 25Hz average processing rate, sufficient to deal with fast-stimuli
and other quick changing visual entries
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Future Work Validate ball detection through shape detection
Recognition of other elements, such as the ones present at the Robocup competition
Explore alternative techniques of Visual Servoing
Study the influence of the robot’s movement on the visual information and on the tracking system’s performance
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Thank you for your atention
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