The Robotics Institute

Carnegie Mellon Robotics Institute

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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.

Keywords

Machine Learning, Mobile Robotics, Autonomous Science, Computer Vision

Notes

Sponsor: NASA ASTEP

Grant ID: NNG0-4GB66G and NAG5-12890

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Science Autonomy

Number of pages: 12

Text Reference

David R. Thompson, Scott Niekum, Trey Smith, and David Wettergreen, "Automatic Detection and Classification of Geological Features of Interest," IEEE Aerospace Conference Proceedings, Big Sky Montana, 2005, March, 2005.

BibTeX Reference

@inproceedings{Thompson_2005_4972,
   author = "David R Thompson and Scott Niekum and Trey Smith and David Wettergreen",
   title = "Automatic Detection and Classification of Geological Features of Interest",
   booktitle = "IEEE Aerospace Conference Proceedings, Big Sky Montana, 2005",
   month = "March",
   year = "2005",
}

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