1. Large-Scale 3D Terrain Modeling David L. Page Mongi A. Abidi, Andreas F. Koschan Sophie Voisin, Sreenivas Rangan, Brad Grinstead, Wei Hao, Muharrem Mercimek Imaging, Robotics, & Intelligent Systems Laboratory The University of Tennessee March 23, 2004

2. March 23, 2006 Slide 2 Outline 3D Terrain Modeling –UTK mobile terrain scanning system –Simulation needs and Army benefit –Scanning system pipeline –“Knoxville Proving Grounds” –Research problems

3. March 23, 2006 Slide 3 UTK Mobile Terrain Scanning System Multi-sensor data collection system for road surface. GPS Receiver GPS Base Station Video Camera 3-Axis IMU and Computer 3D Range Sensor

4. March 23, 2006 Slide 4 Data Acquisition 1 3 2 4 5 6 7 8 1 – Riegl LMS-Z210 Laser Range Scanner 2 – SICK LMS 220 Laser Range Scanner 3 – JVC GR-HD1 High Definition Camcorder 4 – Leica GPS500 D-RTK Global Positioning System 5 – XSens MT9 Inertial Measurement Unit 6 – CPU for acquiring SICK, GPS, and IMU data 7 – CPU for acquiring Riegl data 8 – Power system Modular System Mounted here on a push cart. Geo-referenced geometric 3D model of an area near IRIS West in Knoxville. Actual Path Scanned Path

5. March 23, 2006 Slide 5 3D View of Terrain (Jump to 3D Viewer)

6. March 23, 2006 Slide 6 Outline 3D Terrain Modeling –UTK mobile terrain scanning system –Simulation needs and Army benefit –Scanning system pipeline –“Knoxville Proving Grounds” –Research problems –Static scanning

7. March 23, 2006 Slide 7 Simulation Needs for Terrain Modeling Visualization –Typical terrains only available in 30x30 m 2 grids –Probably sufficient with bump mapping System analysis –Requires high-resolution terrains! –Multi-body dynamics –Linear analysis, PSD Time series analysis –Requires high-resolution terrains! –Multi-body dynamics –Motion stands Discussions with Dr. Al Reid Bump Mapping Why needed, in general?

8. March 23, 2006 Slide 8 Benefit to U.S. Army Scanning 3D terrains is a significant enhancement over traditional towed- cart profiling, cart dynamics, 1D profile, etc. Real terrain modeling overcomes potential limitations of linearity, stationarity, and normality assumptions, particularly associated with PSD (Chaika & Gorsich 2004). Research in 3D processing (tools!) addresses relevant issues in… –data reduction (Al Reid), –terrain analysis (3D EMD), –interpolation, etc.

9. March 23, 2006 Slide 9 Profilometers Four (4) wheel trailer Drawn by a tow vehicle Front axle free to rotate about yaw axis (other constrained) Linkage to draw bar of tow vehicle Rear axle free to rotate about roll axis (other constrained) No compliant suspension components between axles and frame Inertial gyroscope measures pitch and roll angle Ultrasonic measurement between axle and terrain (always points down) Shaft encoder every 0.1 in. of travel Data acquisitions every 3 inches Towed Trailer Profilometer UTK 3D Terrain Modeling Highly correlated sensor data (GPS, IMU, Range) = Correction for vehicle dynamics

10. March 23, 2006 Slide 10 Comparison to Profilometer Path is 300 m length +/- 0.5 cm resolution Path Overlaid on Aerial View Zoom View 2 m wide x 8 m length Video Data of Zoom Notice Cracks in Pavement 120-360 profiles over a 2-8 m swath (3D surface) vs. 1 profile (1D signal) Correlated data vs. trailer dynamics Agile path vs. linear path (?) 3D vs. 1D

11. March 23, 2006 Slide 11 Outline 3D Terrain Modeling –UTK mobile terrain scanning system –Simulation needs and Army benefit –Scanning system pipeline –“Knoxville Proving Grounds” –Research problems

12. March 23, 2006 Slide 12 3D range sensorsPosition and orientation sensorsVisualThermal 3D Position and Orientation Leica -GPSXsens IMU Range Profiles SICKRIEGL IVP Video Sequence Inter-profile Alignment Multi-sensor Visualization Multi-sensor Alignment Multi-modal Data Integration SonyIndigo System Block Diagram

13. March 23, 2006 Slide 13 UTK IRIS Lab 3D Sensors 3D Rendering Sheet-of-light triangulation-based systemStructured-light stereo system Time-of-flight Principle of operation S1S1 X x’ x c’ c S1S1 S2S2 S 1 and S 2 are two sensors. Laser Camera IVP RANGER SC-386 Genex 3D CAM SICK LMS200

14. March 23, 2006 Slide 14 Statistical Modeling of Sensors Roll MeasurementsPitch Measurements Yaw Measurements Standard Deviation = 0.0336Standard Deviation = 0.0338Standard Deviation = 0.0492 Extensive GPS and IMU error characterization and modeling.

15. March 23, 2006 Slide 15 Outline 3D Terrain Modeling –UTK mobile terrain scanning system –Simulation needs and Army benefit –Scanning system pipeline –“Knoxville Proving Grounds” –Research problems

16. March 23, 2006 Slide 16 “Knoxville Proving Grounds” Blue Line is the GPS Path for the loops that we collected. Visualization tool built to be able to visualize “z” measurements Cornerstone Drive, off Lovell Road, I-40 Exit #374 Knoxville, Tennessee, Knoxville Each loop a length of 1.1 mile, Total distance covered on scanning that day = 2.2 miles ( 2 times) = 4.4 miles of the same data. The color of the GPS path encodes the height of the terrain. Over 4 miles = ~2 GB of data

17. March 23, 2006 Slide 17 Data Collection Automated correction for varying speeds and dynamics of platform.

18. March 23, 2006 Slide 18 Elevation Change of Terrain Pathways – Loop scanning 17 m 0 m 17 m 0 m Full length scanning

19. March 23, 2006 Slide 19 High Accuracy 3D Terrain Full Data ~10 km Zoom ~1 km Zoom ~10 m Aerial View

20. March 23, 2006 Slide 20 Triangulated Terrain Mesh The entire stretch, 1.8 meters

21. March 23, 2006 Slide 21 Campus Loop YY Latitude and Longitude Measurements from the Leica DGPS Raw Point Cloud

22. March 23, 2006 Slide 22 Outline 3D Terrain Modeling –UTK mobile terrain scanning system –Simulation needs and Army benefit –Scanning system pipeline –“Knoxville Proving Grounds” –Research problems

23. March 23, 2006 Slide 23 Interprofile Registration Problem GPS curve sampled at 10 Hz. Range Profiles @ 30 Hz 4m wide SICK 2000 Hz and 50cms wide IVP Video recorded at 30 frames/sec IMU data @ 100 Hz Raw Data Vehicle (Scanning) Direction

24. March 23, 2006 Slide 24 Data Interpolation Correct for non-uniform data collection with terrain modeling.

25. March 23, 2006 Slide 25 Pose Localization Video SequenceFeature Matching R, T Pose From Motion GPS drop-outs under certain conditions. Improve overall localization accuracy. RANSAC Filtering Oriented Tracks Filtering

26. March 23, 2006 Slide 26 Data Reduction Noise Removal Adaptive Simplification Original model 363843 triangles 185345 points Reduced to 25% 90893 triangles 48595 points Reduced to 2.5% 9075 triangles 6642 points Initial ModelMultiresolution Analysis and Denoising

27. March 23, 2006 Slide 27 Statistical Modeling of Terrain Empirical mode decomposition of the terrain sample shown above. EMD implementation : Modified Brad’s functions The profile is non-linear and non-stationary but all the IMF’s taken separately are linear and stationary, which means the PSD of the IMF’s model the data better than the PSD of the profile alone. Dataset from near IRIS West The total length of the patch: 20 meters with inter-profile spcaing around 1 cm. The 3D terrain was generated using our system mounted on a van. Reconstructed 3D profile from the statistical model Mean Longitudinal profile

28. March 23, 2006 Slide 28 Pipeline of 3D Reconstruction Camera Calibration Image Rectification Dense Matching Disparity Estimation Triangulation & Visualization Temporal-Based Stereo Tire-Soil Terrain Modeling Calibration Test Setup Disparity Map Input

29. March 23, 2006 Slide 29 3D Model of Military Tire Tire 150 cm dia., 30 cm width Final Model Model Integration (+/- 0.5 mm) Registration (18 Sections, 7 Views)

30. March 23, 2006 Slide 30 Questions? x (m) y (m) z (m) 17 m 0 m Pathways – Loop scanning 17 m 0 m