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Accession Number:
AD1135204
Title:
Accurate Covariance Estimation for Pose Data from Iterative Closest Point Algorithm
Report Date:
2021-03-25
Abstract:
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.
Document Type:
Conference:
Journal:
Pages:
66
File Size:
5.63MB
Contracts:
Grants:
Distribution Statement:
Approved For Public Release
