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[Description] [Support]
[Data Download] [Scripts] [Attribution] [Acknowledgements] Description
Notice (Mar 18, 2019) - We have moved! This dataset was formerly hosted on Carnegie Mellon University servers as the Planetary Pits and Caves 3D Dataset. A change in data archival policy and relocation of the original author (Dr. Uland Wong) to NASA Ames Research Center resulted in the removal of public access. As the data was funded through NASA grants, it was decided to find a new permanent home on NASA servers. An added benefit is that there are now resources to provide maintenance and updates from our new work. The CAVES project at CMU developed robotic capabilities for exploration and 3D mapping of pits and skylights, which are giant holes on the surfaces of planets which may lead to the scientific holy grail of intact lava tubes. Over a two year period, starting in September 2012, many terrestrial analog sites were surveyed with high resolution LIDAR scanning in support of robot testing, algorithmic development and ground truthing. These analog sites either represent the size and shape of skylights/pits on planetary bodies or are geologically significant in their own right. As a part of end-of-project dissemination, this dataset is being released to the public to facilitate robotics and exploration research in this domain. Data presented here are point cloud models which were created using a high-end LIDAR survey scanner, typically from the FARO line (x130, x330). The scanner is stationary and mounted on a tripod. Many view points and scan locations are stitched to create a unified model of the site. Manually selected features were typically utilized for rough matching followed by ICP for fine registration. A robotic total station, which tracks the scan location, was sometimes utilized to facilitate this process. The models are colorized utilizing the onboard camera of the scanner under natural illumination. As sequential scanning often took hours, the illumination may have changed significantly between parts of the unified model. We have taken steps to normalize brightness across the scans utilizing naive voxel-based histogramming. Other color correction schemes are left to end user implementation. Several sites were also imaged with a DSLR camera for photographic data quality beyond the sensor's onboard camera. Examples of successful research based on this data are available here, here and here. Dataset ChangelogMar 18, 2019 - Relocation of data to NASA and info update
Dec
28, 2014 - Addition of Fieg Highwall volume model Dec
18, 2014 - Addition of Sheepridge
volume model Dec 15, 2014 - Initial release of dataset Support and Disclaimer
This data is offered as a courtesy to researchers. No guarantees are made regarding the content, quality or accuracy of any file downloaded from this website, so use at your own risk! For technical questions, please email the current maintainer Uland Wong <uland dot wong at nasa dot gov>. Data
Download
The dataset is partitioned into several downloads, one for each analog site. Data for each site contains *.f32 point cloud files. Refer to the accompanying matlab scripts for examples of how to read the .f32 data files. Data is stored as sequential, 7-attribute readings, in binary little-endian float32 format. There are no delimiting characters or values. The format of the .f32
file is: The first three attributes are the Cartesian coordinates of an individual point in the cloud. Units of the point clouds are in meters. Colors are stored in attribute #5-7, and range from [0,255]. They are either RGB or grayscale (R=G=B). If infrared reflectance data was recorded, it is stored in attribute #4, otherwise zeros are recorded. To
aid in understanding of this data, we have also included a
screen shot of the point cloud and a color photograph of
each site. These files are distributed as .rar
archives.
ScriptsWe
have included several Matlab
files to aid in use and understanding of this dataset. Two
utility files for reading and writing *.f32 files are write_pts_binary_float32.m
and read_pts_binary_float32.m.
The format is otherwise very simple and intended for
painless I/O in any language. We encourage conversion of
point data to more efficient proprietary formats once
downloaded and extracted. Some simple functions for downsampling
point clouds and operating on voxels have also been included
to get started. Download
Matlab
scripts [7 KB] Attribution
This dataset can only be used for research or academic purposes. Any commercial use of the data whatsoever or incorporation of the data into a larger database intended for public distribution must be done with the explicit written consent of the dataset authors. Until then, please use the following attribution if you use the data: Uland Wong, Warren Whittaker, Heather Jones, Red Whittaker. NASA Planetary Pits and Caves Analog Dataset. December, 2014. Acknowledgement
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