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

Motivation  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 2

Motivation  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 3

SnoMote 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 4

SnoMote Sensor Node 5

Mendenhall Glacier Tests 6

Environment  Low contrast  Slope-based hazards  Exposed mountain peaks  Crevasse  Blue ice 7

Multi-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 8

Visual Navigation  Terrain Assessment and Localization  Low contrast, snow-covered terrain  Methods for testing and evaluating through simulation 9

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 10

Glacial Terrain Assessment 11

Glacial Visual SLAM  Standard Visual SLAM system  Major Challenge – Feature Extraction  Region Extraction  Contrast Enhancement  SIFT Features 12

Glacial Visual SLAM 13

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 14

Simulation 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 15

Simulation 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 16

Simulation Design 17

Assessing Simulation Quality  Easy to perform a qualitative comparison  “That looks good…”  A method is needed to compare qualitatively 18

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 19

Assessing Simulation Quality 20 Region ExtractionFeature Count

Conclusions  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 21

Future 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 22

Acknowledgments  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 23

References  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,  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,  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,  ——, “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 Aerospace, Atlanta, GA, April 2010, 24