From my knowledge if you fit geometrical objects into point clouds you want in general minimize the squared distances of the point cloud to your fitted objects. I do so with the downhill simplex method by minimizing the average squared distance. I fit cylinders. In rare input scenarios I end up having not good fitted cylinders - detected by visual validation. Those cylinders have one thing in common. Their axis is not perpendicular to the points's normals. I can modify my error term by multiplying the squared distance of a point to a cylinder with the cosinus of the angle between the point's normal and the cylinder axis. This cosinus is 1 in the worst case and 0 in the best case.
I am looking for a name of this technique and/or publications. I think the most generic formualtion of what I am doing would be:
Modify the squared euclidean distance between input and output accounting to a change in curvature.