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![Page 1: Stephen Williams and Ayanna M. Howard Human-Automation Systems Lab Georgia Institute of Technology Evaluation of Visual Navigation Methods for Lunar Polar.](https://reader035.fdocuments.in/reader035/viewer/2022081515/56649e8e5503460f94b91b26/html5/thumbnails/1.jpg)
Stephen Williams and Ayanna M. Howard
Human-Automation Systems Lab
Georgia Institute of Technology
Evaluation of Visual Navigation Methods for Lunar Polar Rovers in Analogous Environments
Evaluation of Visual Navigation Methods for Lunar Polar Rovers in Analogous Environments
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MotivationMotivation Recent lunar missions have focused on the
search for water ice near the poles LCROSS Clementine Lunar Prospector
On-orbit sensing still requires sensor measurement validation The only direct, unambiguous way is through
ground-based data However, there has never been a landed
mission to this region
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MotivationMotivation Earth-centric satellite sensing suffers from
similar issues Surface data validation even more critical due to
complex atmospheric effects Glacial regions are of particular importance
More sensitive to climate change mechanisms Poorly modeled by other observed surfaces Remote, harsh climate leads to few validation
trials
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SnoMote ProjectSnoMote Project Fixed weather stations do exist
in Greenland and Antarctica Sparse coverage of glacial surface
Scientists interested in the ability to collect dense measurements in targeted locations
This project focused on developing enabling technologies for a glacial mobile weather station
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SnoMote Sensor NodeSnoMote Sensor Node
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Mendenhall Glacier TestsMendenhall Glacier Tests
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EnvironmentEnvironment
Low contrast Slope-based hazards
Exposed mountain peaks Crevasse Blue ice
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Multi-agent ResearchMulti-agent Research Distributed Task Allocation (Antidio Viguria)
Fault tolerant Closer to the global optimal than standard
algorithms Embedded Graph Grammars (Brian Smith)
Enables nodes to self-assemble and maintain desired network topology
Accounts for the dynamics of the platform
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Visual NavigationVisual Navigation Terrain Assessment and Localization
Low contrast, snow-covered terrain
Methods for testing and evaluating through simulation
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Glacial Terrain Assessment
Glacial Terrain Assessment
Estimate the directionality of small-scale surface features within a small region
Based on methods used for Fingerprint Analysis
Determines the Least Squares Estimate of the dominate 2-D Fourier Spectrum direction
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Glacial Terrain Assessment
Glacial Terrain Assessment
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Glacial Visual SLAMGlacial Visual SLAM
Standard Visual SLAM system
Major Challenge – Feature Extraction Region Extraction Contrast
Enhancement SIFT Features
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Glacial Visual SLAMGlacial Visual SLAM
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Evaluation Using Simulation
Evaluation Using Simulation
Ground truth unknown for real environment Local scale terrain topology is unavailable Commodity GPS has significant uncertainty
Simulation system solves these issues Visual quality of simulation impacts the results of
visual algorithms
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Simulation DesignSimulation Design Presented work based on Gazebo open
source simulation system Real surface topologies used
SRTM data for terrestrial sites LRO data for lunar sites
Satellite imaging used to “paint” the terrain
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Simulation DesignSimulation Design Photo-realistic background applied using a
“skybox” Background images created from panorama of
test site Local scale texture blended with main terrain
coloration High frequency components extracted from
photography of test site
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Simulation DesignSimulation Design
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Assessing Simulation Quality
Assessing Simulation Quality
Easy to perform a qualitative comparison “That looks good…”
A method is needed to compare qualitatively
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Assessing Simulation Quality
Assessing Simulation Quality
Any visual algorithm may be applied to images from either simulation or the real terrain
A performance metric can be used to evaluate the effectiveness a specific algorithm
If the difference in performance results are not statistically significant, then the simulation may be viewed as sufficient
Must be evaluated on each algorithm-metric pair
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Assessing Simulation Quality
Assessing Simulation Quality
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Region Extraction Feature Count
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ConclusionsConclusions Real-time vision-based processing techniques were
presented Implemented to cope with image characteristics of glacial
terrain Time and expense of field deployments in remote
regions prevent frequent trials Ground truth data difficult to obtain in real
environments Visually faithful simulation system developed to test
and validate vision-based algorithms Analysis conducted to assess the visual quality of the
simulation
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Future WorkFuture Work
Further testing and validation of simulation assessment method
Development of a simulated “science sensor” to enable testing of science-driven control behaviors Spatial and temporal data interpolation Include noise models
Opportunistic SnoMote testing in Anchorage, Alaska
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AcknowledgmentsAcknowledgments NASA Earth Science Technology Office
provided funding for this work under the Applied Information Systems Technology Program
Dr. Magnus Egerstedt, Georgia Institute of Technology, provided his experience in multi-agent formations
Dr. Matt Heavner, Associate Professor of Physics, University of Alaska Southeast, provided his expertise in glacial field work
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ReferencesReferences B. P. Gerkey, R. T. Vaughan, and A. Howard, “The Player/Stage project:
Tools for Multi-Robot and distributed sensor systems,” in International Conference on Advanced Robotics, ICAR, Coimbra, Portugal, July 2003, pp. 317–323.
L. Hong, Y. Wan, and A. Jain, “Fingerprint image enhancement: Algorithm and performance evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777–789, 1998.
A. M. Reza, “Realization of the contrast limited adaptive histogram equalization (CLAHE) for Real-Time image enhancement,” The Journal of VLSI Signal Processing, vol. 38, no. 1, pp. 35–44, 2004.
S. Williams and A. M. Howard, “A single camera terrain slope estimation technique for natural arctic environments,” in IEEE International Conference on Robotics and Automation, ICRA, Pasadena, CA, May 2008, pp. 2729–2734.
——, “Developing monocular visual pose estimation for arctic environments,” Journal of Field Robotics, vol. 27, no. 2, pp. 145–157, 2009.
——, “Towards visual arctic terrain assessment,” in International Conference on Field and Service Robotics, FSR, Cambridge, MA, July 2009
S. Williams, S. Remy, and A. M. Howard, “,” in American Institute of Aeronautics and Astronautics Conference Infotech @ Aerospace, Atlanta, GA, April 2010,
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