next generation 4-d distributed modeling and visualization of battlefield

Next Generation 4-D Next Generation 4-D Distributed Modeling and Distributed Modeling and Visualization of BattlefieldVisualization of Battlefield

Avideh ZakhorUC BerkeleyJune, 2002

Participants Avideh Zakhor, (UC Berkeley) Bill Ribarsky, (Georgia Tech) Ulrich Neumann (USC) Pramod Varshney (Syracuse) Suresh Lodha (UC Santa Cruz)

Battlefield VisualizationGoal: Detailed, timely and accurate picture of the modern battlefield Many sources of info to build “picture”:

Archival data, roadmaps, GIS and databases: static Sensor information from mobile agents at different times and location: dynamic. Multiple modalities: fusion

How to make sense of all these without information overload?

Major Challenges: Data

Disparate/conflicting sources Large volumes. Inherently uncertain: resulting models also

uncertain. Need to be visualized on mobiles with limited

capability. Time varying, time dependent and dynamic.

Mobile AR VisualizationLaser--------Lidar--------Radar-------

Camera------GPS---------Maps--------Gyroscope--

3D model constructionwith texture

VisualizationDatabase

Model update

Mobiles with augmented reality sensors

Fusion/DecisionMaking

UNCERTAINTY

Research Agenda

Model construction and updateSensor tracking and registrationReal time visualization and multi-model

interactionUncertainty processing and visualization:Fusion used in all of the above.

Visualization Pentagon

InformationFusion

InformationFusion

UncertaintyProcessing/

Visualization

UncertaintyProcessing/

Visualization

4D Modeling/Update

4D Modeling/Update

VisualizationDatabase

VisualizationDatabase

Tracking/Registration

Tracking/Registration

Mobile AR VisualizationLaser--------Lidar--------Radar-------

Camera------GPS---------Maps--------Gyroscope--

3D model constructionwith texture

VisualizationDatabase

Model update

Mobiles with augmented reality sensors

DecisionMaking

Model Construction for Visualization

Develop a framework for 3D model construction for urban areas: Easy, fast, accurate, automatic Compact to represent; Easy to render;

Strategy: Fusion of multiple data sources: intensity, range,

heading, speedometer, panoramic cameras. Incorporate apriori models, e.g. digital

roadmaps. Registration, tracking and calibration.

3D Modeling:

Close-range modeling: Ground based vehicle with

multiple sensors

Far-range modeling: Aerial/satellite imagery Airborne Lidar data

Fusion of close range and far range info at multiple levels: Data and models.

Aerial Data Close-range Data

Fusion

Combining Aerial and Ground Based Models

Highly detailed model of street scenery & building façades

3D Model of terrain and building tops

Complete 3D City Model

Fusion

Airborne Modeling•Laser scans/images from

plane

Ground Based Modeling• Laser scans & images from

acquisition vehicle

• Laser range scanners• Digital roadmaps•Aerial photos

Ground Level Based Data Acquisition

2D laser scanners: horizontal and vertical Intensity camera (UCB)

Hybrid DGPSInertial sensorsCameras (USC)

Results - Point Cloud of Block

Processing Ground Based Laser Data

• Histograms

• Segmentation

• Layer separation

• Interpolation

Resulting Models from Hole Filling

•Before hole filling •After hole filling

Fusing Airborne model with ground based model

Airborne Point cloud

Ground based facade

Merged airborne/façade model

6 DOF Pose Estimation for texture mapping

with 3 DOF pose

with 6 DOF pose

Static Texture Mapping

Copy texture of all triangles into “collage” image

Typical texture reduction: factor 8 - 12

Aerial view of projected image texture (campus of Purdue University)

Sensor Sensor

Image plane

View frustum

Dynamic Texture Projection on LiDAR Data

• Enables Real Time, Multi Source Data Fusion• Requires accurate 3D model, sensor model, and texture/model registration•Tracking and registration algorithms

Mobile AR VisualizationLaser--------Lidar--------Radar-------

Camera------GPS---------Maps--------Gyroscope--

3D model constructionwith texture

VisualizationDatabase

Model update

Mobiles with augmented reality sensors

DecisionMaking

Hierarchical, multiresolution methods forinteractive visualization of extended, detailed urban Scenes

•Data-adapted global quadtree

}Forest of quadtrees tree structure

Data-dependent detailed representation (quadtree depth to level of a “block”)

Block

Façade 1 …

Façade N

LOD Hierarchy

…

…

…

Object M

…

…

…

Object 1

…

…

…

…

…

…

…

City-organized hierarchy

Mobile AR VisualizationLaser--------Lidar--------Radar-------

Camera------GPS---------Maps--------Gyroscope--

3D model constructionwith texture

VisualizationDatabase

Model update

Mobiles with augmented reality sensors

DecisionMaking

Multimodal Interface to Augmented Reality Systems

Speech and gesture multimodal interface test setup

Multimodal interface in action

Infrared lights

Camera with Infrared filter

Gesture pendant(worn on chest)

Demonstration of use of gesture pendant to recognize hand gestures

Mobile AR VisualizationLaser--------Lidar--------Radar-------

Camera------GPS---------Maps--------Gyroscope--

3D model constructionwith texture

VisualizationDatabase

Model update

Mobiles with augmented reality sensors

DecisionMaking

UNCERTAINTY

Visualization of Uncertain Particle Movement

Uncertainty in initial position, direction and speed Uncertainty modeled by Gaussian distribution

Modeling and Visualization of Uncertainty

Spatio-temporal GPS uncertainty models : Number of accessible/used satellites SNR (Signal to Noise Ratio) DOP (Dilution of Precision)

Real-time visualization of GPS-tracked objects and associated uncertainty within VGIS

Low Uncertainty Line Preserving Compression

Original Unconstrained Coastline preserving

Hierarchical Line Simplification

Intersection preserving simplification

Mobile AR VisualizationLaser--------Lidar--------Radar-------

Camera------GPS---------Maps--------Gyroscope--

3D model constructionwith texture

VisualizationDatabase

Model update

Mobiles with augmented reality sensors

Fusion/DecisionMaking

Bayesian Networks with Temporal Updates

Information flow

Presence of atarget

Readings ofSensor 1

Readings ofSensor 2

Readings ofSensor 3Presence at a

later time

Report fromprocessor 1

Report fromprocessor 2

Objective: To incorporate time-dependence of observations

and evidence in Bayesian inference networks.

Temporal Fusion in Multi-Sensor Target Tracking Systems

For a multi-sensor tracking system, sensors can be either synchronous or asynchronous (temporally staggered)

T: Sampling interval of synchronous sensors

T1: Time difference between sensor 1 and sensor 2 in asynchronous-sensor case

T=T1+T2

Transitions (1)

Government:Interactions with AFRL, ONR, NASA, NIMAPresentations to President Bush and Gov. RidgePresentations to program directors at STRICOM

Industry:Raytheon, Lockheed Martin, Boeing, SarnoffHJW, Sick, Bosch, Astech, Airborne 1Sensis, Andro computing solutionsOlympusRhythm and Hues Studio

Publications (1)

C. Früh and A. Zakhor, "3D model generation for cities using aerial photographs and ground level laser scans", Computer Vision and Pattern Recognition, Hawaii, USA, 2001, p. II-31-8, vol.2.  

H. Foroosh, “ A closed-form solution for optical flow by imposing temporal constraints”, Proceedings 2001 International Conference on Image Processing, vol.3, pp .656-9. 

C. Früh and A. Zakhor, "Data processing algorithms for generating textured 3D building façade meshes from laser scans and camera images”, accepted to 3D Data Processing, Visualization and Transmission, Padua, Italy, 2002 

John Flynn, “Motion from Structure: Robust Multi-Image, Multi-Object Pose Estimation”, Master’s thesis, Spring 2002, U.C. Berkeley 

S. You, and U. Neumann. “Fusion of Vision and Gyro Tracking for Robust Augmented Reality Registration,” IEEE VR2001, pp.71-78, March 2001

B. Jiang, U. Neumann, “Extendible Tracking by Line Auto-Calibration,” submitted to ISAR 2001

J. W. Lee. “Large Motion Estimation for Omnidirectional Vision,” PhD thesis, University of Southern California, 2002

Publications (2)

J. W. Lee, B. Jiang, S. You, and U. Neumann. “Tracking with Vision for Outdoor Augmented Reality Systems,” submitted to IEEE Journal of Computer Graphics and Applications, special edition on tracking technologies, 2002  

William Ribarsky, “Towards the Visual Earth,” Workshop on Intersection of Geospatial and Information Technology, National Research Council (October, 2001).

William Ribarsky, Christopher Shaw, Zachary Wartell, and Nickolas Faust, “Building the Visual Earth,” to be published, SPIE 16th International Conference on Aerospace/Defense Sensing, Simulation, and Controls (2002).

David Krum, William Ribarsky, Chris Shaw, Larry Hodges, and Nickolas Faust “Situational Visualization,” pp. 143-150, ACM VRST 2001 (2001).

David Krum, Olugbenga Omoteso, William Ribarsky, Thad Starner, and Larry Hodges “Speech and Gesture Multimodal Control of a Whole Earth 3D Virtual Environment,” to be published, Eurographics-IEEE Visualization Symposium 2002. Winner of SAIC Best Student Paper award.

William Ribarsky, Tony Wasilewski, and Nickolas Faust, “From Urban Terrain Models to Visible Cities,” to be published, IEEE CG&A.

David Krum, Olugbenga Omoteso, William Ribarsky, Thad Starner, and Larry Hodges “Evaluation of a Multimodal Interface for 3D Terrain Visualization,”submitted to IEEE Visualization 2002.

Publications (3) Justin Jang, William Ribarsky, Chris Shaw, and Nickolas Faust, "View-Dependent

Multiresolution Splatting of Non-Uniform Data," pp. 125-132, Eurographics-IEEE Visualization Symposium 2002

C. K. Mohan, K. G. Mehrotra, and P. K. Varshney, ``Temporal Update Mechanisms for Decision Making with Aging Observations in Probabilistic Networks’’, Proc. AAAI Fall Symposium, Cape Cod, MA, Nov. 2001. 

R. Niu, P. K. Varshney, K. G. Mehrotra and C. K. Mohan, `` Temporal Fusion in Multi-Sensor Target Tracking Systems’’, to appear in Proceedings of the Fifth International Conference on Information Fusion, July 2002, Annapolis, Maryland.  

Q. Cheng, P. K. Varshney, K. G. Mehrotra and C. K. Mohan, ``Optimal Bandwidth Assignment for Distributed Sequential Detection’’, to appear in Proceedings of the Fifth International Conference on Information Fusion, July 2002, Annapolis, Maryland. 

Suresh Lodha, Amin P. Charaniya, Nikolai M. Faaland, and Srikumar Ramalingam, "Visualization of Spatio-Temporal GPS Uncertainty within a GIS Environment" to appear in the Proceedings of SPIE Conference on Aerospace/Defense Sensing, Simulation, and Controls, April 2002. 

Suresh K. Lodha, Nikolai M. Faaland, Amin P. Charaniya, Pramod Varshney, Kishan Mehrotra, and Chilukuri Mohan, "Uncertainty Visualization of Probabilistic Particle Movement", To appear in the Proceedings of The IASTED Conference on Computer Graphics and Imaging", August 2002. 

Publications (4)

Suresh K. Lodha, Amin P. Charaniya, and Nikolai M.Faaland, "Visualization of GPS Uncertainty in a GIS Environment", Technical Report UCSC-CRP-02-22, University of California, Santa Cruz, April 2002, pages 1-100. 

Suresh K. Lodha, Nikolai M. Faaland, Grant Wong, Amin Charaniya, Srikumar Ramalingam, and Arthur Keller, "Consistent Visualization and Querying of Geospatial Databases by a Location-Aware Mobile Agent", In Preparation, to be submitted to ACM GIS Conference, November 2002.  

Suresh K. Lodha, Nikolai M. Faaland, and Jose Renteria, ``Hierarchical Topology Preserving Simplification of Vector Fields using Bintrees and Triangular Quadtrees'', Submitted for publication to IEEE Transactions on Visualization and Computer Graphics. 

Lilly Spirkovska and Suresh K. Lodha, ``AWE: Aviation Weather Data Visualization Environment'', Computers and Graphics, Volume 26, No.~1, February 2002, pp.~169--191. 

Suresh K. Lodha, Krishna M. Roskin, and Jose Renteria, ``Hierarchical Topology Preserving Compression of 2D Terrains'', Submitted for publication to Computer Graphics Forum.

Publication (5)

Suresh K. Lodha and Arvind Verma, ``Spatio-Temporal Visualization of Urban Crimes on a GIS Grid'',Proceedings of the ACM GIS Conference, November 2000, ACM Press, pages 174--179. 

Christopher Campbell, Michael Shafae, Suresh K. Lodha and D. Massaro, ``Multimodal Representations for the Exploration of Multidimensional Fuzzy Data", Submitted for publication to Behavior Research, Instruments, and Computers. 

Suresh K. Lodha, Jose Renteria and Krishna M. Roskin, ``Topology Preserving Compression of 2D Vector Fields'', Proceedings of IEEE Visualization '2000, October 2000, pp. 343--350. 

Cross Collaboration

UCB USC G.T. SYR UCSC

Model const. & texture

X X x

Tracking & reg. & pose est.

X X x

Visualization,rendering

x x X x

Uncertain.processing

x X x

Uncertain.Visualization.

x x X

Outline of Talks

A. Zakhor, U.C. Berkeley, "Overview" C. Freuh, U.C. Berkeley, "Fast 3D model construction of urban

environments" U. Neuman, USC, "Tracking and Data Fusion for 4D Visualization" Bill Ribarsky, Georgia Tech, "4D Modeling and Mobile

Visualization" Lunch Pramod Varshney, Syracuse, "Temporal Uncertainty Computation,

Fusion, and Visualization in Multisensor Environments" S. Lodha, U.C. Santa Cruz, "Uncertainty Quantification and

Visualization: Mobile Targets within Geo-Spatially Registered Terrains"

Discussion, Feedback from Government