Visual terrain mapping for Mars exploration

  • Authors:

  • Clark F. Olson;Larry H. Matthies;John R. Wright;Rongxing Li;Kaichang Di

  • Affiliations:

  • Computing and Software Systems, University of Washington, Bothell, 18115 Campus Way NE, Box 358534, Bothell, WA 98011-8246, USA;Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, USA;Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, USA;Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 2070 Neil Avenue, 470 Hitchcock Hall, Columbus, OH 43210-1275, USA;Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 2070 Neil Avenue, 470 Hitchcock Hall, Columbus, OH 43210-1275, USA

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2007

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Abstract

One goal for future Mars missions is for a rover to be able to navigate autonomously to science targets not visible to the rover, but seen in orbital or descent images. This can be accomplished if accurate maps of the terrain are available for the rover to use in planning and localization. We describe techniques to generate such terrain maps using images with a variety of resolutions and scales, including surface images from the lander and rover, descent images captured by the lander as it approaches the planetary surface, and orbital images from current and future Mars orbiters. At the highest resolution, we process surface images captured by rovers and landers using bundle adjustment. At the next lower resolution (and larger scale), we use wide-baseline stereo vision to map terrain distant from a rover with surface images. Mapping the lander descent images using a structure-from-motion algorithm generates data at a hierarchy of resolutions. These provide a link between the high-resolution surface images and the low-resolution orbital images. Orbital images are mapped using similar techniques, although with the added complication that the images may be captured with a variety of sensors. Robust multi-modal matching techniques are applied to these images. The terrain maps are combined using a system for unifying multi-resolution models and integrating three-dimensional terrains. The result is a multi-resolution map that can be used to generate fixed-resolution maps at any desired scale.