Imaging, Robotics, and Intelligent Systems
IRIS
3D Reconstruction of Tire-soil Interaction
Wei Hao
Imaging, Robotics, & Intelligent Systems Laboratory
The University of Tennessee
Mar 23, 2004
July 12, 2016 Slide 2
Possible Sponsor Needs
Imaging, Robotics, and Intelligent Systems
IRIS
- Modeling the real world tire-soil interaction
• Applying state of art computer vision techniques to generate accurate 3D model of the real
world tire-soil interaction.
- Our models can be used for real-time measurement of the tire deformation
• With the 3D modeling of the tire deformation, it is easy to get real data for tire design.
- Our method is a new perspective to tire-soil interaction simulation problem
• efforts of establish analytical models of the tire-soil interaction has been made by several
research groups;
• Our method will provide real data set of the interaction which can be used as validation model.
-The method proposed here is applicable for all kinds of soil
• Analytical models for soft soil and general terrains are very difficult to be established;
• Lab data set based on mechanical sensors are also not easily obtained;
• Fast digitizing of the tire-soil interaction provides accurate models of the whole process.
July 12, 2016 Slide 3
Imaging, Robotics, and Intelligent Systems
Related Research Work
IRIS
-
Modeling and simulation of real time tire-soil interaction is demanded from military and
defense industries, mainly for mobility analysis.
-
Researchers has built numerical models for tire and terrain based on techniques such as
finite element analysis. Integration of tire and terrain dynamic models constitutes the tiresoil interaction model.
kinematic tire models are built with wheel slip ratio, slip angle and normal force in
consideration.
-
The kinematic models are numerical simulation rather than real world.
-
Computer vision and photogrammetry techniques are matured enough to provide accurate
and real time digitizing of real world tire-terrain interaction, which cannot be achieved by
mechanical analysis and numerical modeling.
[Lee etal 05] Lee, J.H., Liu, Q., and Zhang, T., “Predictive Semi-Analytical Model for Tire-Snow
Interaction”, SAE2005-01-0932, 2005
[Pan 05] W. Pan, “Modeling Tire Transient Response in Real-Time Simulation”, NSF I/UCRC Center
for Virtual Proving Ground 17th Semiannual Conference, Iowa City, Iowa, April 5-6, 2005
[Wal etal 04]Walsh, SDC and Tordesillas, “A thermomechanical approach to the development of
micropolar constitutive models for granular media” Acta Mechanica, 167 (3-4), 2004
July 12, 2016 Slide 4
3D Reconstruction of Tire-Soil
Interaction
Imaging, Robotics, and Intelligent Systems
IRIS
Motivations:
Digitize the tire-soil interaction and build a 4D movie of it (3D reconstruction time series) using
state-of-arts techniques of computer vision.
Fig 1. Typical scene of tire-soil interaction to be
modeled. (Courtesy of US ARMY)
Objectives
- Provide new method of tire-soil interaction study other than mechanism modeling.
- Build automated data acquisition system which can digitize the tire-soil interaction process in real time;
- Develop robust algorithms which can generate time series of 3D reconstruction;
- Build models of soil-tire interaction for on-road and off-road and test them using real data.
July 12, 2016 Slide 5
Diagram of Data Acquisition
System
Imaging, Robotics, and Intelligent Systems
IRIS
Data Collection
System GUI
Date Collection
Program
Camera synchronization
control
Console/Computer
Frame Grabber
Software
Camera 1
Camera 2
Fig 2. Data Acquisition System Diagram.
…
Camera n
Hardware
July 12, 2016 Slide 6
Data Acquisition System
Implementation
Stereo Vision System
Sensor Placement
Video of Data Acquisition (1)
Video of Data Acquisition (2)
Imaging, Robotics, and Intelligent Systems
IRIS
July 12, 2016 Slide 7
Imaging, Robotics, and Intelligent Systems
Sample Data Collections(1)
• We collected stereo image sequences under difference
circumstances.
Fig 3. Stereo image sequence of tire running on soft soil.
IRIS
July 12, 2016 Slide 8
Imaging, Robotics, and Intelligent Systems
Sample Data Collections(2)
Fig 4. Stereo image sequence of tire running on grassy soil.
IRIS
July 12, 2016 Slide 9
Imaging, Robotics, and Intelligent Systems
Sample Data Collections(3)
Fig 5. Stereo image sequence of tire running on soft soil.
IRIS
July 12, 2016 Slide 10
Imaging, Robotics, and Intelligent Systems
Sample Data Collections(4)
Fig 6. Stereo image sequence of tire running on gravel road.
IRIS
July 12, 2016 Slide 11
Imaging, Robotics, and Intelligent Systems
3D Reconstruction Software
Camera
Calibration
Image
Rectification
IRIS
- Matured 3D reconstruction techniques available
for static scene reconstruction;
- We are now developing advanced temporal
stereo techniques to generate 3D model sequence.
Dense
Matching
Disparity
Estimation
Triangulation &
Visualization
Fig 7. 3D reconstruction based on multiple view geometry
July 12, 2016 Slide 12
Imaging, Robotics, and Intelligent Systems
Stereo Camera Calibration
IRIS
-The relative translation and rotation of the two cameras are constant
during the data acquisition processing.
-Camera calibration method: [Zhang 1998]
Fig 8. Frame 10, 12,13,14 of the
different pose of the planar
calibration rig of the left camera.
[Zhang 1998] Z. Zhang. A Flexible New Technique for Camera Calibration. Technical Report MSRTR-98-71, Microsoft Research,
Dec 1998.
July 12, 2016 Slide 13
Imaging, Robotics, and Intelligent Systems
IRIS
Stereo Camera Calibration
Calibration Results:
The intrinsic parameters for the two stereo cameras and the relative displacement of the two
cameras.
Stereo calibration parameters:
Intrinsic parameters of left camera:
Focal Length:
fc_left = [ 1135.33835 1134.75205 ] ± [ 14.29820 13.98266 ]
Principal point:
cc_left = [ 518.01575 371.44273 ] ± [ 23.44983 16.16967 ]
Skew:
alpha_c_left = [ 0.00000 ] ± [ 0.00000 ]
Distortion:
kc_left = [ -0.01518 -0.27005 -0.00295 -0.01034 0.00000 ] ± [ 0.04661
0.00000 ]
0.33772
0.00391
0.00641
0.29916
0.00383
0.00316
Intrinsic parameters of right camera:
Focal Length:
fc_right = [ 1138.66134 1141.58652 ] ± [ 13.88601 13.73518 ]
Principal point:
cc_right = [ 488.89808 326.89759 ] ± [ 15.09664 16.76655 ]
Skew:
alpha_c_right = [ 0.00000 ] ± [ 0.00000 ]
Distortion:
kc_right = [ -0.14638 0.53836 -0.01497 -0.00728 0.00000 ] ± [ 0.04115
0.00000 ]
Extrinsic parameters (position of right camera wrt left camera):
Rotation vector:
Translation vector:
om = [ -0.05570 0.01084 -0.00898 ] ± [ 0.01026 0.02087 0.00129 ]
T = [ -102.38391 4.02867 4.61723 ] ± [ 0.46797 0.32744 3.34022 ]
July 12, 2016 Slide 14
Relative Pose of the Calibration Rig
with Respect to the Cameras
Fig 9. Position of All the 6*6 feature regions w.r.t the stereo
system computed.
Imaging, Robotics, and Intelligent Systems
IRIS
July 12, 2016 Slide 15
Imaging, Robotics, and Intelligent Systems
Image Rectification
IRIS
- Rectification can simplify the dense matching process. After
rectification, the correspondence will be of the same x-coordinates.
- Use the method of [Fusiello etal 2000]. It require good
synchronization between the calibration image pairs.
Fig 10. Rectified Frame 10,
12,13,14 of the left camera.
[Fusiello etal 2000] Andrea Fusiello, Emanuele Trucco, Alessandro Verri. “A compact algorithm for rectification of stereo pairs”,
Machine Vision and Applications(2000) 12:16-22
July 12, 2016 Slide 16
Imaging, Robotics, and Intelligent Systems
IRIS
Dense Matching
Stereo matching methods:
Local Matching Methods:
Global Methods:
-Block Matching;
-Dynamic Programming;
-Gradient-based Optimization
-Intrinsic Curves
-Feature Matching
-Graph Cuts
-Nonlinear Diffusion
-Belief Propagation
-Correspondenceless Methods
July 12, 2016 Slide 17
Imaging, Robotics, and Intelligent Systems
Sample Dense Matching Results
Sample inputs captured by the same data acquisition set:
Fig 11. Sample inputs of stereo images
IRIS
July 12, 2016 Slide 18
Imaging, Robotics, and Intelligent Systems
Dense Matching Outputs
(a)
Fig 12. Sample output of (a) SAD method
(b)
(b) SSD+DP Method
IRIS
July 12, 2016 Slide 19
Imaging, Robotics, and Intelligent Systems
Temporal Stereo Techniques
IRIS
Stereo vision and structure from motion are two well formed techniques for 3D reconstruction.
In current computer vision research, combining both spatial and temporal image
information is becoming increasingly popular as a way of improving the robustness and
computational efficiency of recovering the 3-D structure of a scene.
Related work:
[Dav03] J. Davis, R. Ramamoorthi, and S. Rusinkiewicz. Spacetime stereo: A unifying framework for depth
from triangulation. In CVPR, 2003.
[Zha 03] Li Zhang, Brian Curless, and Steven M. Seitz. Spacetime Stereo: Shape Recovery for Dynamic Scenes.
In Proceedings of CVPR), Madison, WI, June, 2003, pp. 367-374.
[Leu 04] Leung, C. and Appleton, B. and Lovell, Brian C. and Sun, C. An Energy Minimisation Approach to Stereo-Temporal
Dense Reconstruction. In ICPR 2004
July 12, 2016 Slide 20
Imaging, Robotics, and Intelligent Systems
Future Work
- Add more sensors into the data acquisition system;
- collect more data set for different soil;
- Finish the software and generate the 3D models and the 4D movie.
IRIS