Estimating Pose Uncertainty for Surface Registration

Accurate registration of surfaces is a common problem in computer vision. Several algorithms exist to refine an approximate value for the pose to an accurate value. They are all more or less variants of the Iterated Closest Point algorithm of Besl and McKay (1992). Up to now the problem of determining the uncertainty in the pose estimate thus obtained has not been addressed in detail. In this paper we present a framework in which to quantify the uncertainty in pose. We introduce a new parameter called the registration index to give a simple means of quantifying the pose errors one might expect when registering a particular shape. 1 Introduction To appear at British Machine Vision Conference, Edinburgh, UK. (1996). The registration of surfaces and curves can be broken down into two separate tasks. A matching task in which an approximate estimate of the pose is obtained. This might also involve a large database of model shapes. Then a registration task in which an accurate po...