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3D ROAD PROFILING USING STEREOGRAPHY Theunis Botha

Schalk Els

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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

height colour map

Calibration surface

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Depth Map

RMS error is 2.51mm

Measured Surface

Colour map

Error Surface

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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 a Power

Kriging

Inpainting Fast Marching Method

RMS error

6.616 6.082 5.561

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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

Combined Section RMS error = 2.4mm

Single Section RMS error = 2.85mm

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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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