ICP General Comprehension

• The goal of ICP is to find the rigid transformation T that best aligns a cloud of scene points with a  geometric model.
• The alignment process work is to minimize the mean squard distance between scene points and their cloest model point.
• ICP is efficient, with average case complexity of O(nlogn) for n point images, and it converges monotonically to a local minimum.
• At each iteration, the algorithm computes correspondences by finding cloest points, and then minimizes the mean square error in positon between the correspondences.
• Since ICP is an iterative descent algorithm, it requires a good initial estimate in order to converge to the global minimum, and all scene points are assumed to have correspondences in the model.