VALIDATION AND TUNNING OF DENSE STEREO-VISION SYSTEMS USING HI-RESOLUTION 3D REFERENCE MODELS

Transcription

1 VALIDATION AND TUNNING OF DENSE STEREOVISION SYSTEMS USING HIRESOLUTION 3D REFERENCE MODELS E. REMETEAN (CNES) S. MAS (MAGELLIUM) JB GINESTET (MAGELLIUM) L.RASTEL (CNES) ASTRA

2 CONTENT Validation methodology Hardware & software tools Preliminary results Study status Credits 2

3 Dense stereovision system validation To build a navigation map and compute a safe and effective trajectory, the Autonomous Navigation software needs a reliable knowledge of the rover surroundings Study goals 3D reconstruction accuracy Robustness wrt the scene content & lighting Impact of the stereovision system parameters Optimal parameter set definition for a given robotic mission Methodology Comparison of the computed disparity maps to dense reference maps acquired with low parallax 3

4 Acquisition Mechanical Ground Support Equipment Acquisition of stereo images & reference models with reduced parallax Composed of a stereobench, a Laser Scanner, translation linear stages FARO Photon 20 Laser Scanner Up to 30 million 3D points / sb f.o.v. Accuracy ±2mm Measurement range up to 20m Translation stages for parallax minimization 4

5 Acquisition MGSE calibration Estimation of transfer matrix between the Laser Scanner and stereobench reference frames Parallax minimisation Stereo Bench (SB) Laser Scanner (LS) Calibration performed after any MGSE displacement on the Mars yard Laser Tracker (LT) Laser Tracker used to measure Position of the landmark balls Position & attitude of the stereobench Accuracy better than 0.1mm Landmark Balls Useful area 5

6 Acquisition campaigns 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) 6

7 Laser Scanner data filtering 3DFilter software was developed for: Interest zone selection (corresponding to stereobench field of view) Points clouds filtering for measurement artefacts & outliers removal 7

8 Data exploitation: Perception Workshop Comparison of real disparity (physical stereobench) to virtual disparity (virtual stereobench 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 stereobench physical parameters (adjustable) Initial virtual stereobench position & attitude obtained from MGSE calibration step 8

9 Data exploitation: PW Stereobenchmarking Similarity scores Classical windowbased scores (SAD, SSD, ZNCC) 3Ddistance score (a little pessimistic) Virtual 3D cloud Real 3D cloud Left optical centre 3Ddist = DepthReal DepthVirtual Virtual stereobench position & attitude optimisation Stereo base length optimisation indirect stereo base length measurement method 9

10 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) 10

11 Preliminary results (1/3) Accuracy (3Ddist) 100mm stereo base CCD 4.65µm pixels Full resolution images Scenes Mean Error (mm) Std dev (mm) Indoor Outdoor x7 correlation window Virtual SB attitude & base optimisation to measure intrinsic performance Mean Error < Autonomous Navigation DEM cell size (40mm) Impact of image resolution Image subsampling Pixel size (µm) Mean error (mm) Mean accuracy degradation Number of pixels to process 1/ % 100% 1/ % 25% 1/ % 6.25% 11

12 Preliminary results (2/3) Impact of stereocorrelation window size Correlation window size Mean error (mm) Mean accuracy gain wrt 7x7 Estimated complexity wrt 7x7 9x % +65% 7x % 0% 5x % 51% Robustness to exposure time L\R 5 ms 48 ms 81 ms 87 ms 135 ms 170 ms 5 ms ms ms ms ms ms Correlation ratio (%) 12

13 Preliminary results (3/3) Fast multiresolution stereocorrelation algorithm L\R 5 ms 48 ms 81 ms 87 ms 135 ms 170 ms 5 ms ms ms ms ms ms L\R 5 ms 48 ms 81 ms 87 ms 135 ms 170 ms 5 ms ms ms ms ms ms Correlation ratio (%) Mean error (% wrt monoresolution algorithm) 13

14 Study status Today Validation methodology adapted for dense stereovision systems Indirect stereo base estimation method Accuracy of the studied stereovision system is compatible with AN requirements Good robustness to image exposure conditions Fast multiresolution algorithm will become the new CNES baseline Further work Fullresolution outdoor acquisition campaigns Performances with stereobench flight model demonstrator Tough textures campaigns Stereo base length impacts Security margins definition for Autonomous Navigation 14

15 Credits : CNES subcontractors involved Stereobenches Flight model demonstrator: CSEM / MCSE Ground models: COMAT Aerospace, AR2P Perception Workshop Magellium CSSI 3DFilter CSSI Validation studies & MGSE realisation Magellium SudRectif 15

16 Thank you for your attention! 16