| 29 | | |
| | 29 | \begin{enumerate} |
| | 30 | \item |
| | 31 | For our easy goal we hope to be able to reliably classify pixels within a single static image as either obstacle, line, or grass. We will implement an algorithm for classifying pixels as line or obstacle and grass based on a `safe zone.' It then classifies obstacles and lines or grass using texture. Combining these classifiers should give reliably sepperate between our three classifications. |
| | 32 | \item |
| | 33 | Our more difficult goal will be to take these classified pixels and make them into useful information on the robots environment. For the obstacle pixels we will identify objects full objects. Then use a perspective transformation or approximation to find their distance and location relative to the robot. For the lines we will use a hough transform to find the equations of visible, broken, and partially hidden lines. Lines will also require an as yet determined approximation for curved regions. |
| | 34 | \item |
| | 35 | Our stretch goal will be to take all our work, performed in matlab, and port it into opencv, with the goal of using on an actual robot in real time. |
| | 36 | \end{enumerate} |
