One of the fundamental problems of robotics and navigation is the estimation of relative pose of an external object with respect to the observer. A common method for computing the relative pose is the Iterative Closest Point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in down-stream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this thesis a novel method for estimating uncertainty from sensed data is introduced. This method was exercised in a virtual simulation of an automated aerial refueling (AAR) task. While prior work assumed the sensor itself had been carefully characterized a-priori, the introduced method learns the sensor uncertainty from live data, making the proposed approach more computationally efficient and robust to sensor degradation than prior techniques.