Theunis Botha, Schalk Els, Vehicle Dynamics Group, University of Pretoria

1. 12/20/2013
3D ROAD PROFILING USING STEREOGRAPHY
Theunis Botha
Schalk Els
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2. 12/20/2013
Background
For vertical dynamic, the road input is the main source of
excitation.
In simulations a realistic road profile is vital.
Furthermore if the road input represents a real road, simulations
can be validated with experimental results.
Thus, the ability to measure road profiles is important in off road
vehicle dynamics.
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Current Measurement Techniques
Static:
Using a LiDAR/Sonar sensor in a static position to measure a
surface using a gimbal or X-Y table. Method is accurate but can
only measure small regions.
Dynamic:
Using a vehicle mounted sensor to measure lateral line scans
which are then pieced together longitudinally using an IMU/INS.
Method can measure large areas but requires an accurate and
expensive IMU/INS system.
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Presented Method
Using stereography to capture a small grid of points at every
sample, these grids overlap and are pieced together using 3
registration techniques. Reduces the accuracy and cost of the
required IMU/INS system
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Stereovision Basics
Determines disparity
between two images of the
same scene with slight offset
Disparity is mapped to 3D
coordinates using simple
camera models
Implemented using OpenCv
library
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Depth Map
Calibration surface
Calibration surface
height colour map
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Depth Map
Measured Surface
Colour map
Error Surface
RMS error is 2.51mm
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Interpolation
Based on a Fast Marching Algorithm used to inpaint images.
Iterative obtains the pixel which is closest the propagating front and
interpolates the pixel value based on neighbouring pixel values and
gradient
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Interpolation
Comparative study between the Inverse to a Power method and Kriging
shows better accuracy and reduced computational cost.
Disadvantage: Works on gridded 3D points only.
Inverse to
Kriging
a Power
RMS
error
Inpainting
Fast
Marching
Method
6.616
5.561
6.082
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Joining of 3D Maps
Features in the images are tracked between frames, each feature has a
3D coordinate relative to the camera. The relative orientation and
translation matrix (M) between frames are obtained in a least squares
mean. The rotation and translations are accumulated such that each
frame is relative to the first.
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Joining of 3D Maps
Two additional registration techniques are used which uses the 3D points:
3d features descriptors at Keypoints is used to preform additional
alignment
Iterative Closest Point (ICP) algorithm is used to perform slight adjustment
to remove small misalignments.
Only using the point cloud and bitmap are used to align the frames.
Drift may occur over time resulting in a slightly offset surface, thus while
no INS/IMU is required, a slow sampling, inexpensive INS/IMU will help to
reduce drift over long periods.
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Joining of 3D Maps
Single Section
RMS error = 2.85mm
Combined Section
RMS error = 2.4mm
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Conclusion
A method of profiling a road using inexpensive cameras was shown
providing reasonable accuracy and reducing the requirement of a high
accuracy and expensive INS/IMU.
An interpolation technique applicable to grid points is shown providing
better accuracy and faster solving time as compared to the most
frequently used interpolation techniques.
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14. 12/20/2013
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