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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. Outline. 3D Terrain Modeling - PowerPoint PPT Presentation
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I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Large-Scale 3D Terrain Modeling
David L. PageMongi A. Abidi, Andreas F. Koschan
Sophie Voisin, Sreenivas Rangan, Brad Grinstead, Wei Hao, Muharrem MercimekImaging, Robotics, & Intelligent Systems Laboratory
The University of TennesseeMarch 23, 2004
March 23, 2006 Slide 2
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Outline
• 3D Terrain Modeling– UTK mobile terrain scanning system– Simulation needs and Army benefit– Scanning system pipeline– “Knoxville Proving Grounds”– Research problems
March 23, 2006 Slide 3
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
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
March 23, 2006 Slide 4
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Data Acquisition
1
3
2
4
5
6
7
8
1 – Riegl LMS-Z210 Laser Range Scanner
2 – SICK LMS 220 LaserRange Scanner3 – JVC GR-HD1 High Definition Camcorder4 – Leica GPS500 D-RTK
Global Positioning System5 – XSens MT9 Inertial Measurement Unit6 – CPU for acquiring SICK, GPS, and IMU data7 – CPU for acquiring Riegl data8 – Power system
Modular SystemMounted here on a push cart.
Geo-referenced geometric 3D model of an area near IRIS West in Knoxville.
Actual Path
Scanned Path
March 23, 2006 Slide 5
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
3D View of Terrain(Jump to 3D Viewer)
March 23, 2006 Slide 6
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Outline
• 3D Terrain Modeling– UTK mobile terrain scanning system– Simulation needs and Army benefit– Scanning system pipeline– “Knoxville Proving Grounds”– Research problems– Static scanning
March 23, 2006 Slide 7
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Simulation Needs for Terrain Modeling
• Visualization– Typical terrains only
available in 30x30 m2 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?
March 23, 2006 Slide 8
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
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.
March 23, 2006 Slide 9
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
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
March 23, 2006 Slide 10
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Comparison to Profilometer
Path is 300 m length +/- 0.5 cm resolution
Path Overlaid on Aerial View
Zoom View2 m wide x 8 m length
Video Data of ZoomNotice 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
March 23, 2006 Slide 11
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Outline
• 3D Terrain Modeling– UTK mobile terrain scanning system– Simulation needs and Army benefit– Scanning system pipeline– “Knoxville Proving Grounds”– Research problems
March 23, 2006 Slide 12
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
3D range sensors Position and orientation sensors Visual Thermal
3D Position and Orientation
Leica -GPS Xsens IMU
Range Profiles
SICKRIEGL IVP
Video Sequence
Inter-profile Alignment
Multi-sensorVisualization
Multi-sensor Alignment
Multi-modal Data Integration
Sony Indigo
System Block Diagram
March 23, 2006 Slide 13
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
UTK IRIS Lab 3D Sensors
3D Rendering
Sheet-of-light triangulation-based system Structured-light stereo system Time-of-flight
Principle of operation
S12/t*sr
X
x’xc’c
S1 S2S1 and S2 are two sensors.
LaserCamera
tan sf
s- tan f B)s(r
IVP RANGER SC-386 Genex 3D CAMSICK LMS200
March 23, 2006 Slide 14
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Statistical Modeling of Sensors
Roll Measurements Pitch Measurements Yaw Measurements
Standard Deviation = 0.0336 Standard Deviation = 0.0338 Standard Deviation = 0.0492
Extensive GPS and IMU error characterization and modeling.
March 23, 2006 Slide 15
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Outline
• 3D Terrain Modeling– UTK mobile terrain scanning system– Simulation needs and Army benefit– Scanning system pipeline– “Knoxville Proving Grounds”– Research problems
March 23, 2006 Slide 16
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
“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
March 23, 2006 Slide 17
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Data Collection
Automated correction for varying speeds and dynamics of platform.
March 23, 2006 Slide 18
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Elevation Change of Terrain
Pathways – Loop scanning
17 m
0 m
17 m
0 mFull length scanning
March 23, 2006 Slide 19
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
High Accuracy 3D TerrainFull Data~10 km
Zoom ~1 km
Zoom ~10 mAerial View
March 23, 2006 Slide 20
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Triangulated Terrain Mesh
The entire stretch,
1.8 meters
March 23, 2006 Slide 21
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Campus Loop
Y
Latitude and Longitude
Measurements
from the Leica DGPS
Raw Point Cloud
March 23, 2006 Slide 22
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Outline
• 3D Terrain Modeling– UTK mobile terrain scanning system– Simulation needs and Army benefit– Scanning system pipeline– “Knoxville Proving Grounds”– Research problems
March 23, 2006 Slide 23
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
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
Tr
tr
tr
tt zyxD ],,[
Tg
tg
tg
tt zyxP ],,[
),,(
Raw Data
Vehicle (Scanning) Direction
tttt WPDR
March 23, 2006 Slide 24
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Data Interpolation
1
)(
)(
0111
1)()(
1
1)()( 11
1
111
np
p
nnnn
n
dγ
dγ
λ
W
W
dγdγ
dγdγ
Correct for non-uniform data collection with terrain modeling.
March 23, 2006 Slide 25
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Pose Localization
Video Sequence Feature Matching
R, T
Pose From Motion
GPS drop-outs under certain conditions.Improve overall localization accuracy.
)))1(1log(/)1(log( pεΓceilN
RANSAC Filtering
n
i
i
h
XxK
nhxpdf
1)(
1)(
Oriented Tracks Filtering
March 23, 2006 Slide 26
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Data Reduction
Noise Removal
Adaptive Simplification
Original model
363843 triangles185345 points
Reduced to 25%
90893 triangles48595 points
Reduced to 2.5%
9075 triangles6642 points
Initial Model Multiresolution Analysis and Denoising
March 23, 2006 Slide 27
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
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
March 23, 2006 Slide 28
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Pipeline of 3D Reconstruction
Camera Calibration
Image Rectification
Dense Matching
Disparity Estimation
Triangulation &Visualization
Temporal-Based StereoTire-Soil Terrain Modeling
Calibration
2211
22112
1
txtxtx
txdtxdedtxcdE,~,,
),,(),,(][
Test Setup
Disparity Map
Input
March 23, 2006 Slide 29
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
3D Model of Military Tire
Tire 150 cm dia., 30 cm width
Final Model
Model Integration(+/- 0.5 mm)
Registration(18 Sections, 7 Views)
March 23, 2006 Slide 30
I R I SImaging, Robotics, and Intelligent Systems
I R I SImaging, Robotics, and Intelligent Systems
Questions?
x (m)y (m)
z (m)
17 m
0 m
Pathways – Loop scanning
17 m
0 m