Automatic Detection and Classification of Geological Features of Interest
Conference Paper, Proceedings of IEEE Aerospace Conference, pp. 366 - 377, March, 2005
Abstract
The volume of data that planetary rovers and their instrument payloads can produce will continue to outpace available deep space communication bandwidth. Future exploration rovers will require science autonomy systems that interpret collected data in order to selectively compress observations, summarize results, and respond to new discoveries. We present a method that uses a probabilistic fusion of data from multiple sensor sources for onboard segmentation, detection and classification of geological properties. Field experiments performed in the Atacama desert in Chile show the system's performance versus ground truth on the specific problem of automatic rock identification.
BibTeX
@conference{Thompson-2005-9121,author = {David R. Thompson and Scott Niekum and Trey Smith and David Wettergreen},
title = {Automatic Detection and Classification of Geological Features of Interest},
booktitle = {Proceedings of IEEE Aerospace Conference},
year = {2005},
month = {March},
pages = {366 - 377},
keywords = {Machine Learning, Mobile Robotics, Autonomous Science, Computer Vision},
}
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