User:Eirisu

From Robin

(Difference between revisions)
Jump to: navigation, search
(Master's Thesis:)
(Road Detection)
Line 8: Line 8:
== Road Detection ==
== 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:
 +
 +
<math>\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)</math>
== Obstacle Detection ==
== Obstacle Detection ==

Revision as of 07:25, 22 September 2015

Eirik Sundet

eirisu@ifi.uio.no

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)

Obstacle Detection

Personal tools
Front page