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David R. Thompson, Scott Niekum, Trey Smith, and David S. Wettergreen. In Proc. IEEE Aerospace Conf., March 2005.
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 detection.
@inproceedings{thompson05:sci_observer,
author = "David R. Thompson and Scott Niekum and Trey Smith and David S. Wettergreen",
title = "Automatic Detection and Classification of Geological Features of Interest",
booktitle = "Proc. IEEE Aerospace Conf.",
month = "March",
year = 2005,
}
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