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Automatic detection of dust devils and clouds on Mars

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Abstract

The acquisition of science data in space applications is shifting from teleoperated data collection to an automated onboard analysis, resulting in improved data quality, as well as improved usage of limited resources such as onboard memory, CPU, and communications bandwidth. Science instruments onboard a modern deep-space spacecraft can acquire much more data that can be downloaded to Earth, given the limited communication bandwidth. Onboard data analysis offers a means of compressing the huge amounts of data collected and downloading only the most valuable subset of the collected data. In this paper, we describe algorithms for detecting dust devils and clouds onboard Mars rovers, and summarize the results. These algorithms achieve the accuracy required by planetary scientists, as well as the runtime, CPU, memory, and bandwidth constraints set by the engineering mission parameters. The detectors have been uploaded to the Mars Exploration Rovers, and currently are operational. These detectors are the first onboard science analysis processes on Mars.

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Authors and Affiliations

  1. Jet Propulsion Laboratory, MS 125-209, 4800 Oak Grove Dr., Pasadena, CA, 91109, USA

    Andres Castano, Alex Fukunaga, Jeffrey Biesiadecki, Rebecca Castano & Steve Chien

  2. Department of Geological Sciences, Arizona State University, P.O. Box 871404, Tempe, AZ, 85287, USA

    Lynn Neakrase, Patrick Whelley & Ronald Greeley

  3. Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX, 77843, USA

    Mark Lemmon

Authors
  1. Andres Castano
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  2. Alex Fukunaga
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  3. Jeffrey Biesiadecki
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  4. Lynn Neakrase
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  5. Patrick Whelley
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  6. Ronald Greeley
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  7. Mark Lemmon
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  8. Rebecca Castano
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  9. Steve Chien
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Correspondence to Andres Castano.

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Castano, A., Fukunaga, A., Biesiadecki, J. et al. Automatic detection of dust devils and clouds on Mars. Machine Vision and Applications 19, 467–482 (2008). https://doi.org/10.1007/s00138-007-0081-3

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  • DOI: https://doi.org/10.1007/s00138-007-0081-3

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