User:Eirisu

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Eirik Sundet

eirisu@ifi.uio.no

Contents

Master's Thesis:

  • Stereo Vision and for Unmanned Ground Vehicle

My Master's Thesis concerns the use of optical sensors, in order to construct 3D-models of the area around an autonomous vehicle.

Road Detection

One of the first things they want to achieve the Norwegian Defence Research Establishment (FFI), is an autonomous vehicle able to drive by itself at tarmac roads. Thus, I've begun making an algorithm that can detect where the road is in a color-image.

As a first step, I've chosen to take a segment of the image, and calculate the gaussian model for this section. The assumption is that the road is always right in front of the vehicle. Then, a region-growing search is executed to find the other 8-connected pixels belonging to the gaussian model, according to the Bayesian cost function:

\epsilon = \frac{1}{2\pi^{\frac{n}{2}}*|\Sigma|^{\frac{1}{2}}}*exp\bigg((-0.5)*\frac{(I(n,m) - \mu)'}{\Sigma}*(I(n,m) - \mu)\bigg)

Bayesian Classification on RGB space

The results below is computed using only the RGB Color-Space as features and the bayesian cost function on a 373*113pixel image. The left images shows some good result. In these examples, the road texture is pretty homogeneous, and there is little ambiguity. The two images on the right shows some bad result. In the top image, the section right in front of the car, which is used for making a gaussian model for the road, is dominated by shadows, which causses the gaussian model to be false. Shadows are a problem, but there are methods to handle them. The bottom image shows a situation where there are several areas that displays simmilar RGB values as the road.


frame|left|none|alt=Alt text|An example of good results using the Bayesian Classifier for Road Detection, based only upon the RGB Color-space frame|center|none|alt=Alt text| An example of some bad results

Obstacle Detection

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