Rick H. Yuan

Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Clark N. Taylor, PhD


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.

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DTIC Accession Number