Advanced Lane Finding Project
The goals / steps of this project are the following:
Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
Apply a distortion correction to raw images.
Use color transforms, gradients, etc., to create a thresholded binary image.
Apply a perspective transform to rectify binary image ("birds-eye view").
Detect lane pixels and fit to find the lane boundary.
Determine the curvature of the lane and vehicle position with respect to center.
Warp the detected lane boundaries back onto the original image.
Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle
position.
The code for this step is contained in the first code cell of the IPython notebook (or in lines # through # of the
file called some_file.py ).
I start by preparing "object points", which will be the (x, y, z) coordinates of the chessboard corners in the
world. Here I am assuming the chessboard is fixed on the (x, y) plane at z=0, such that the object points are
the same for each calibration image. Thus, objp is just a replicated array of coordinates, and objpoints
will be appended with a copy of it every time I successfully detect all chessboard corners in a test image.
imgpoints will be appended with the (x, y) pixel position of each of the corners in the image plane with
each successful chessboard detection.
Example Writeup
You can use this file as a template for your writeup if you want to submit it as a
markdown file, but feel free to submit a pdf if you prefer.
Rubric Points
Camera Calibration
1. Have the camera matrix and distortion coefficients been computed correctly and
checked on one of the calibration images as a test?
