Advanced Lane finding
The fourth project in the self-driving car course was a reiteration and improvement of the lane finding project that we did earlier.
The goal was to learn the very basics of computer vision. This includes learning about
- camera calibration using a set of
chessboardimages, like this:
- distortion correction using the calculated distortion of the camera,
- color thresholding, using various color spaces, to obtain a “binary” image which only shows pixels which are likely part of a lane line, so that from the image below,
we get an output like this one:
- perspective transformation for getting a birds-eye view of the road (so that lane lines are parallel in the transformed image),
- detecting lane line pixels on the transformed image using pixel-density histograms and the sliding-window method (identifying areas dense in pixels based on the lower half of the image, and moving upwards in a sliding method, to see where the lanes go)
- determining the fane curvature and the car’s position relative to the center of the lane,
- outputting a visugl estimation of the lane by warping back the calculated area to the original image. The final output looks something like this:
Written on January 7, 2018
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