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Proceedings Paper

Anomalous cases of astronaut helmet detection
Author(s): Chester Dolph; Andrew J. Moore; Matthew Schubert; Glenn Woodell
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Paper Abstract

An astronaut’s helmet is an invariant, rigid image element that is well suited for identification and tracking using current machine vision technology. Future space exploration will benefit from the development of astronaut detection software for search and rescue missions based on EVA helmet identification. However, helmets are solid white, except for metal brackets to attach accessories such as supplementary lights. We compared the performance of a widely used machine vision pipeline on a standard-issue NASA helmet with and without affixed experimental feature-rich patterns. Performance on the patterned helmet was far more robust. We found that four different feature-rich patterns are sufficient to identify a helmet and determine orientation as it is rotated about the yaw, pitch, and roll axes. During helmet rotation the field of view changes to frames containing parts of two or more feature-rich patterns. We took reference images in these locations to fill in detection gaps. These multiple feature-rich patterns references added substantial benefit to detection, however, they generated the majority of the anomalous cases. In these few instances, our algorithm keys in on one feature-rich pattern of the multiple feature-rich pattern reference and makes an incorrect prediction of the location of the other feature-rich patterns. We describe and make recommendations on ways to mitigate anomalous cases in which detection of one or more feature-rich patterns fails. While the number of cases is only a small percentage of the tested helmet orientations, they illustrate important design considerations for future spacesuits. In addition to our four successful feature-rich patterns, we present unsuccessful patterns and discuss the cause of their poor performance from a machine vision perspective. Future helmets designed with these considerations will enable automated astronaut detection and thereby enhance mission operations and extraterrestrial search and rescue.

Paper Details

Date Published: 22 May 2015
PDF: 9 pages
Proc. SPIE 9469, Sensors and Systems for Space Applications VIII, 946906 (22 May 2015); doi: 10.1117/12.2176743
Show Author Affiliations
Chester Dolph, NASA Langley Research Ctr. (United States)
Old Dominion Univ. (United States)
Andrew J. Moore, NASA Langley Research Ctr. (United States)
Matthew Schubert, NASA Langley Research Ctr. (United States)
Christopher Newport Univ. (United States)
National Institute of Aerospace (United States)
Glenn Woodell, NASA Langley Research Ctr. (United States)
Science Systems and Applications, Inc. (United States)


Published in SPIE Proceedings Vol. 9469:
Sensors and Systems for Space Applications VIII
Khanh D. Pham; Genshe Chen, Editor(s)

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