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Carnegie Mellon Robotics Institute

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Abstract

Selective data return leverages onboard data analysis to allocate limited bandwidth resources during remote exploration. Here we present an adaptive method to subsample image sequences for downlink. We treat selective data return as a compression problem in which the explorer agent transmits the subset of mea- surements that are most informative with respect to the complete dataset. Experiments demonstrate selective downlink of navigation imagery by a rover during autonomous geologic investigations in the Atacama desert of Chile. Here automatic analysis identifies informative images using classifications based on natural image statistics. Image texture analysis, together with a context-sensitive Hidden Markov Model representation, permits adaptive downlink in response to geologic unit boundaries. Selective data return improves the science content of returned data for this geologic mapping task.

Notes

Sponsor: NASA

Grant ID: NNG0-4GB66G, NAG5-12890

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Science Autonomy

Number of pages: 6

Text Reference

David R. Thompson, Trey Smith, and David Wettergreen, "Information-Optimal Selective Data Return for Autonomous Rover Traverse Science and Survey," IEEE Conference on Robotics and Automation, May, 2008.

BibTeX Reference

@inproceedings{Thompson_2008_6167,
   author = "David R Thompson and Trey Smith and David Wettergreen",
   title = "Information-Optimal Selective Data Return for Autonomous Rover Traverse Science and Survey",
   booktitle = "IEEE Conference on Robotics and Automation",
   month = "May",
   year = "2008",
}

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