Given a spline, what is an efficient way to find (approximately) the least amount (and position) of key-points to approximate the spline with a line strip, so that the largest distance between the line strip and the spline at any given point is <= d.
Here is a visual example. I’m looking to find a computationally efficient way to find the points in the green circles that I use as beginnings and endings for the lines.
What I thought about is walking along the spline, summing up the traveled distance. As I walk the spline, I test the last position as a key-point candidate. Once the distance traveled on the spline and the length of the line through the key-point candidate deviate by a certain percentage, I use the last key point candidate that was still below that percentage. What I’m uncertain about is first, if there is a more efficient way, and second, deviation in length as a percentage is not really the same as distance (e.g. in cm). If there is a relatively long, relatively straight part of the spline, the length deviation that I might accept, could lead to quite a large distance between the spline and the line somewhere on the way, couldn’t it?