Nao line detection: Difference between revisions

From Intelligent Materials and Systems Lab

(Created page with "== Overview == We need to detect the white lines on the field. The main reason is that they help the robot to localize itself on the field. Line detection, however. is computa...")
 
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== Overview ==
== Overview ==
We need to detect the white lines on the field. The main reason is that they help the robot to localize itself on the field. Line detection, however. is computationally a quite complex task, so we have tried a few different approaches. There are implementations of [http://en.wikipedia.org/wiki/Hough_transform Hough Transform] in the OpenCV library, but they tend to be a bit too general-purpose and slow for our needs. We have also tried [http://en.wikipedia.org/wiki/RANSAC RANSAC], but it become too slow when the number of line points increased. Now we are using Randomized Hough Transform that we implemented by the description of <code>L. Xu, E. Oja, P. Kultanen "A new curve detection method: Randomized Hough transform"</code>
We need to detect the white lines on the field. The main reason is that they help the robot to localize itself on the field. Line detection, however. is computationally a quite complex task, so we have tried a few different approaches. There are implementations of [http://en.wikipedia.org/wiki/Hough_transform Hough Transform] in the OpenCV library, but they tend to be a bit too general-purpose and slow for our needs. We have also tried [http://en.wikipedia.org/wiki/RANSAC RANSAC], but it become too slow when the number of line points increased. Now we are using Randomized Hough Transform that we implemented by the description of <i>L. Xu, E. Oja, P. Kultanen "A new curve detection method: Randomized Hough transform"</i>


== Our implementation ==
== Our implementation ==

Revision as of 19:40, 22 May 2013

Overview

We need to detect the white lines on the field. The main reason is that they help the robot to localize itself on the field. Line detection, however. is computationally a quite complex task, so we have tried a few different approaches. There are implementations of Hough Transform in the OpenCV library, but they tend to be a bit too general-purpose and slow for our needs. We have also tried RANSAC, but it become too slow when the number of line points increased. Now we are using Randomized Hough Transform that we implemented by the description of L. Xu, E. Oja, P. Kultanen "A new curve detection method: Randomized Hough transform"

Our implementation