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Robust visual fiducials for skin-to-skin relative ship pose estimation

Joshua G. Mangelson, Ryan W. Wolcott, Paul Ozog, Ryan M. Eustice

Year
2016
Citations
4

Abstract

This paper reports on an optical visual fiducial system developed for relative-pose estimation of two ships at sea. Visual fiducials are ubiquitous in the robotics literature, however none are specifically designed for use in outdoor lighting conditions. Blooming of the CCD causes a significant bias in the estimated pose of square tags that use the outer corners as point correspondences. In this paper, we augment existing state-of-the-art visual fiducials with a border of circles that enables high accuracy, robust pose estimation. We also present a methodology for characterizing tag measurement uncertainty on a per measurement basis. We integrate these methods into a relative ship motion estimation system and support our results using outdoor imagery and field data collected aboard the USNS John Glenn and USNS Bob Hope during skin-to-skin operations.

Keywords

Fiducial markerPoseArtificial intelligenceComputer visionComputer scienceRoboticsPoint (geometry)RobotMathematics

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