large-scale 3d terrain modeling

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



Outline

• 3D Terrain Modeling– UTK mobile terrain scanning system– Simulation needs and Army benefit– Scanning system pipeline– “Knoxville Proving Grounds”– Research problems




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




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




3D View of Terrain(Jump to 3D Viewer)




Outline

• 3D Terrain Modeling– UTK mobile terrain scanning system– Simulation needs and Army benefit– Scanning system pipeline– “Knoxville Proving Grounds”– Research problems– Static scanning




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?




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.




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




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




Outline





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




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




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.




Outline





“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




Data Collection

Automated correction for varying speeds and dynamics of platform.




Elevation Change of Terrain

Pathways – Loop scanning

17 m

0 m

17 m

0 mFull length scanning




High Accuracy 3D TerrainFull Data~10 km

Zoom ~1 km

Zoom ~10 mAerial View




Triangulated Terrain Mesh

The entire stretch,

1.8 meters




Campus Loop

Y

Latitude and Longitude

Measurements

from the Leica DGPS

Raw Point Cloud




Outline





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




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.




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




Data Reduction

Noise Removal

Adaptive Simplification

Original model

363843 triangles185345 points

Reduced to 25%


Reduced to 2.5%


Initial Model Multiresolution Analysis and Denoising




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




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




3D Model of Military Tire

Tire 150 cm dia., 30 cm width

Final Model

Model Integration(+/- 0.5 mm)

Registration(18 Sections, 7 Views)




Questions?

x (m)y (m)

z (m)

17 m

0 m

Pathways – Loop scanning

17 m

0 m

large-scale 3d terrain modeling

Documents

d terrain modelinghighly

d terrain modelingdavid

terrain analysis

d terrains

d profile

d model

d surface

real terrain modeling