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VALIDATION AND TUNNING OF DENSE STEREO-VISION SYSTEMS
USING HI-RESOLUTION 3D REFERENCE MODELS
E. REMETEAN (CNES)S. MAS (MAGELLIUM) - JB GINESTET (MAGELLIUM) - L.RASTEL (CNES)
ASTRA - 14.04.2011
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CONTENT
Validation methodologyHardware & software toolsPreliminary resultsStudy statusCredits
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Dense stereo-vision system validation
Study goals3D reconstruction accuracyRobustness wrt the scene content & lightingImpact of the stereo-vision system parametersOptimal parameter set definition for a given robotic mission
MethodologyComparison of the computed disparity maps to dense reference maps acquired with low parallax
To build a navigation map and compute a safe and effective trajectory, the Autonomous Navigation software needs a reliable knowledge of the rover surroundings
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Acquisition Mechanical Ground Support Equipment
Acquisition of stereo images & reference models with reduced parallax Composed of a stereo-bench, a Laser Scanner, translation linear stages
FARO Photon 20 Laser ScannerUp to 30 million 3D points / s-b f.o.v.Accuracy ≈ ±2mmMeasurement range up to 20m
Translation stages for parallax minimization
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Acquisition MGSE calibration
Laser Tracker (LT)
Laser Scanner(LS)
Landmark Balls
Stereo Bench(SB)
Useful area
Estimation of transfer matrix between the Laser Scanner and stereo-bench reference framesParallax minimisation
Calibration performed after any MGSE displacement on the Mars yard
Laser Tracker used to measurePosition of the landmark ballsPosition & attitude of the stereo-benchAccuracy better than 0.1mm
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Both indoors and outdoors (scene content & lighting conditions variation) Several exposure times for every scene (robustness studies) The content of the scene is changed rather than moving the MGSE (to avoid MGSE calibrations)
Acquisition campaigns
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Laser Scanner data filtering 3D-Filter software was developed for:
Interest zone selection (corresponding to stereo-bench field of view) Points clouds filtering for measurement artefacts & outliers removal
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Data exploitation: Perception Workshop
Comparison of real disparity (physical stereo-bench) to virtual disparity (virtual stereo-bench looking at the 3D model measured by the LS)
Real disparity computation parameters can be modified from a control panel
The filtered Laser Scanner points cloud is meshed and the disparity is computed using the virtual stereo-bench physical parameters (adjustable)
Initial virtual stereo-bench position & attitude obtained from MGSE calibration step
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Data exploitation: PW – Stereo-benchmarking
Similarity scores Classical window-based scores (SAD, SSD, ZNCC) 3D-distance score (a little pessimistic)
Virtual 3D cloud
Real 3D cloud
Left optical centre
3Ddist = | DepthReal – DepthVirtual |
Virtual stereo-bench position & attitude optimisation Stereo base length optimisation → indirect stereo base length measurement method
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Data exploitation: PW – 3D viewer 3D viewer allows to display in 3D
Real & Virtual points clouds computed from disparities Mismatches between the clouds (local similarity error)
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Preliminary results (1/3)
Accuracy (3Ddist)100mm stereo base CCD 4.65µm pixels Full resolution images 7x7 correlation window Virtual SB attitude & base optimisation to measure intrinsic performance
Scenes Mean Error (mm) Std dev (mm)Indoor 5.05 0.64
Outdoor 13.27 1.55
Image sub-sampling
“Pixel size” (µm)
Mean error (mm)
Mean accuracy degradation
Number of pixels to process
1/1 4.65 13.27 0% 100%
1/2 9.30 16.60 25.09% 25%
1/4 18.60 20.45 54.11% 6.25%
☺ Mean Error < Autonomous Navigation DEM cell size (40mm)
Impact of image resolution
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Preliminary results (2/3)
Impact of stereo-correlation window sizeCorrelation window size Mean error (mm)
Mean accuracy gain wrt 7x7
Estimated complexity wrt 7x7
9x9 12.45 6.18% +65%
7x7 13.27 0% 0%5x5 15.00 -13.04% -51%
Robustness to exposure time
Correlation ratio (%)
L \ R 5 ms 48 ms 81 ms 87 ms 135 ms 170 ms5 ms 27.24 - - - - -55 ms - 88.50 66.87 57.10 15.33 7.2790 ms - 75.03 89.68 81.58 47.92 28.70
100 ms - 62.31 82.68 90.09 77.61 55.69150 ms - 19.64 57.60 82.59 90.18 86.70200 ms - 8.10 32.99 55.30 87.42 90.19
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Preliminary results (3/3) Fast multi-resolution stereo-correlation algorithm
L \ R 5 ms 48 ms 81 ms 87 ms 135 ms 170 ms5 ms 81.90 - - - - -
55 ms - 92.98 91.11 92.49 86.82 82.7190 ms - 92.74 93.08 92.48 91.14 88.96
100 ms - 93.11 92.22 93.11 92.79 91.89150 ms - 91.97 92.11 92.94 93.19 92.98200 ms - 90.33 91.26 92.23 92.90 93.15
L \ R 5 ms 48 ms 81 ms 87 ms 135 ms 170 ms5 ms +24.5 - - - - -
55 ms - -7.6 -7.4 -10.1 -0.8 +2.190 ms - -9.3 -8.7 -7.2 -12.7 -14.9
100 ms - -14.7 -7.7 -9.4 -11.7 -13.6150 ms - -13.5 -13.0 -11.8 -9.1 -9.2200 ms - -17.7 -18.0 -17.6 -9.1 -9.2
Correlation ratio (%)
Mean error(% wrt mono-resolution
algorithm)
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Study statusToday
Validation methodology adapted for dense stereo-vision systemsIndirect stereo base estimation methodAccuracy of the studied stereo-vision system is compatible with AN requirementsGood robustness to image exposure conditionsFast multi-resolution algorithm will become the new CNES baseline
Further workFull-resolution outdoor acquisition campaignsPerformances with stereo-bench flight model demonstrator“Tough” textures campaignsStereo base length impactsSecurity margins definition for Autonomous Navigation
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Credits : CNES sub-contractors involved
Stereo-benchesFlight model demonstrator: CSEM / MCSEGround models: COMAT Aerospace, AR2P
Perception WorkshopMagelliumCS-SI
3D-FilterCS-SI
Validation studies & MGSE realisationMagelliumSud-Rectif
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Thank you for your attention!
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