Director, Intelligent Robotics Group
NASA Ames Research Center
It gives me great pleasure to announce the release of the "Apollo Zone" Digital Image Mosaic (DIM) and Digital Elevation Model (DEM). These maps cover approx. 18% of the Lunar surface at a resolution of 1024 pixels per degree (approx 30 m/pixel). The maps are the result of 3 years worth of work by the NASA Ames Intelligent Robotics Group (IRG) to align and process more than 4,000 images from the Apollo Metric Camera (AMC), which flew aboard Apollo 15, 16, and 17. The AMC images were provided by the Apollo Image Archive at Arizona State University.
To preview the "Apollo Zone" maps, download the following "KML" file for viewing in Google Earth:
http://byss.ndc.nasa.gov/stereopipeline/dataviz/apollo_metric.kml
Once you open that file in Google Earth you will have options to view these "Apollo Zone" maps overlaid on Google Earth's "Moon mode". The full maps (in GeoTIFF format with complete metadata) have also been uploaded to the Lunar Mapping and Modeling Project (LMMP) portal (http://lmmp.nasa.gov) and will soon be available for visualization and download via that site.
The "Apollo Zone" maps cover the following sites of interest: Apollo 15, Apollo 16, Alphonsus Crater, Rima Prinz, Aristarchus Plateau-2, Ina D Caldera, Sulpicius Gallus, Mare Crisium, Mare Smythii, King Crater, Tsiolkovskiy Crater, Aitken Crater, and half of Van de Graaf Crater.
The terrain model has an average vertical accuracy of 40 m/pixel and standard deviation of 37 m (compared to LOLA laser altimetry tracks). Over 46% of the covered surface has vertical errors lower than 25 m.
The "Apollo Zone" maps (image, elevation, hillside, colorshade, confidence and precision) were automatically generated using new computer vision algorithms developed by IRG:
- robust statistical sub-pixel stereo correspondence
- robust bundle adjustment and radiometric corrections for large-scale
image mosaics
- orbital camera position/orientation estimation using interest point
extraction
- photometric correction of exposure time, shadow removal and generation of
seamless large-scale image mosaics.
- photometric method for reconstructing lunar albedo
- photoclinometric terrain reconstruction method that improves lunar
DTM precision
- statistical method for multiple stereo digital terrain model mosaicking
- multi-view 3D terrain reconstruction
- DTM / LOLA alignment and lidar / image matching
These algorithms have been released as NASA open-source (Ames Stereo Pipeline, Neo-Geography Toolkit, and NASA Vision Workbench). Map processing was performed using the NASA Pleiades supercomputer. In addition to the Apollo Metric Camera images, the fully automatic map processing pipeline has also been used with data from the Lunar Reconnaissance Orbiter Camera (LROC) and by several planetary science groups.
This work was funded by the Lunar Mapping and Modeling Project (LMMP). We gratefully acknowledge the support of our collaborators at NASA MSFC, NASA GSFC, JPL and USGS. We sincerely thank Mark Robinson and the Apollo Image Archive at ASU for restoring and bringing the AMC data to "digital life". Our special thanks go to Ray French and Mark Nall for their support and leadership of LMMP.
If you have any questions, or would like more information, please let me know.
- robust statistical sub-pixel stereo correspondence
- robust bundle adjustment and radiometric corrections for large-scale
image mosaics
- orbital camera position/orientation estimation using interest point
extraction
- photometric correction of exposure time, shadow removal and generation of
seamless large-scale image mosaics.
- photometric method for reconstructing lunar albedo
- photoclinometric terrain reconstruction method that improves lunar
DTM precision
- statistical method for multiple stereo digital terrain model mosaicking
- multi-view 3D terrain reconstruction
- DTM / LOLA alignment and lidar / image matching
These algorithms have been released as NASA open-source (Ames Stereo Pipeline, Neo-Geography Toolkit, and NASA Vision Workbench). Map processing was performed using the NASA Pleiades supercomputer. In addition to the Apollo Metric Camera images, the fully automatic map processing pipeline has also been used with data from the Lunar Reconnaissance Orbiter Camera (LROC) and by several planetary science groups.
This work was funded by the Lunar Mapping and Modeling Project (LMMP). We gratefully acknowledge the support of our collaborators at NASA MSFC, NASA GSFC, JPL and USGS. We sincerely thank Mark Robinson and the Apollo Image Archive at ASU for restoring and bringing the AMC data to "digital life". Our special thanks go to Ray French and Mark Nall for their support and leadership of LMMP.
If you have any questions, or would like more information, please let me know.
Cheers,
Terry Fong
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