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Science-Centric Sampling Approaches of Geo-Physical Environments for Realistic Robot Navigation

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Science-Centric Sampling Approaches of Geo-Physical Environments for Realistic Robot Navigation SCIENCE-CENTRIC SAMPLING APPROACHES OF GEO-PHYSICAL ENVIRONMENTS FOR REALISTIC ROBOT NAVIGATION A Thesis Presented to The Academic Faculty by Lonnie T. Parker, IV In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Electrical and Computer Engineering Georgia Institute of Technology August 2012 Copyright c 2012 by Lonnie T. Parker, IV SCIENCE-CENTRIC SAMPLING APPROACHES OF GEO-PHYSICAL ENVIRONMENTS FOR REALISTIC ROBOT NAVIGATION Approved by: Professor Ayanna M. Howard, Advisor Professor Jeff Shamma School of Electrical and Computer School of Electrical and Computer Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Professor Fumin Zhang Professor Mike Stilman School of Electrical and Computer College of Computing Engineering Georgia Institute of Technology Georgia Institute of Technology Professor George Riley Date Approved: June 15, 2012 School of Electrical and Computer Engineering Georgia Institute of Technology DEDICATION Based upon the infallible word of God, I dedicate this work to Him, according to Proverbs 16:3. iii ACKNOWLEDGEMENTS Before anyone else, I want to acknowledge and thank my Lord and Savior, Jesus Christ, whom I am in and who lives within me. This degree and this experience would not be possible without His provision, favor, and grace. Especially in the moments when I did not believe it was possible to finish, my faith in Him pushed me forward. Next, to my wife, Andrea, who has been my constant encourager, supporter, and living reminder that I could complete this journey, thank you. Simply your presence in my life has made this walk all the more bearable. I love you. Next, to my parents, Lonnie III and Rosalia, thank you for your unwaivering support and consistent con- cern for me as I transitioned into adulthood during this degree. To my other family from across the country and around the way, Octavia, Vincent and Isabella “Izzy” Grimes, to the Bakers, and to the Beavers, thank you for welcoming me into your lives and for expanding my network of loved ones who have been praying to our God on my behalf. Thank you to Dr. Ayanna Howard for your support and commitment to me as an advisor and thank you to my thesis committee. To the funding organiza- tions that have generously provided me with fellowships, scholarships, stipends and grants to support my research and fund my degree during my tenure at Georgia Tech: the Science, Mathematics, and Research for Transformation (SMART) Program, the Naval Undersea Warfare Center, the FACES program, the President’s Office, and the National Aeronautics and Space Administration (NASA) under the Earth Science and Technology Office, Applied Information Systems Technology Program. To Dean Gary May, Dr. Comas Haynes, Dr. Raheem Beyah, the faculty and staff on the FACES Steering committee, the Office of Minority Educational Development, and the FOCUS program, thank you for helping to create a welcoming environment that iv enables students like me to succeed at Georgia Tech. I am forever grateful for the experience. Lastly, I want to thank my brothers in Christ, Dr. Brad Jones, Dr. J. Chris Ford, Wendell C. Smith, and everyone from the Christian Organization of Dy- namic Engineers (CODE). For your prayers in faith and for opening up your hearts to myself and one another, I thank you for your commitment to our Lord and I pray that your trust in Him will grow unbounded. Continue to exhibit not those qualities common to men of this world, but instead, those fruits of the Holy Spirit, common to men of God, love, joy, patience, peace, kindness, goodness, faithfulness, gentleness, and self-control. v TABLE OF CONTENTS DEDICATION ................................... iii ACKNOWLEDGEMENTS ............................ iv LISTOFTABLES ................................. viii LISTOFFIGURES ................................ x SUMMARY.....................................xviii I INTRODUCTION.............................. 1 II BACKGROUND&MOTIVATION..................... 4 2.1 Traditional Spatial Sampling of Geo-physical Data . ..... 4 2.2 ReconstructionThroughInterpolation . .. 7 2.3 EnvironmentScopeandSamplingSpace . 9 2.3.1 PracticalConsiderations . 10 2.4 Previous Robotic Environmental Monitoring Systems and Applications .............................. 11 2.5 Broader Context: Mapping through Sampling . 17 III METHODOLOGY: METRICS FOR SAMPLING SYSTEM ANALYSIS 20 3.1 SpatialEnvironmentandSpecifications. .. 20 3.1.1 Discretizationandquantization . 21 3.2 Classification-basedInterpolation . ... 25 3.2.1 EstimationandProbability . 25 3.2.2 Improvement over traditional estimation . 29 3.3 Configuration Ranking: Expected and Actual Error . .. 34 3.4 Application of Performance and Resource Constraints . ..... 39 3.4.1 AverageNearestNeighborDistance. 39 IV ESTABLISHING A NAVIGATION METHODOLOGY . 44 4.1 Sampling Configurations via Robot Navigation . 44 4.1.1 VettingUniqueNavigationStrategies. 45 vi 4.2 EstablishingaBaselineNavigationMethod . 51 4.2.1 Lawnmower-traditionalnavigation . 52 4.2.2 Lawnmower-randomnavigation . 53 4.2.3 Lawnmower-informednavigation . 54 V EXPERIMENTAL SETUP AND RESULTS . 61 5.1 Modeling Spatial Data: Simulation and Validation Tools ...... 61 5.1.1 RMSErrorMetricinSpatialSampling . 61 5.1.2 MappingAccuracyStandards . 62 5.1.3 Creating Simulated Digital Elevation Maps . 64 5.1.4 ValidationoftheDEMMaker. 69 5.2 ValidationofContributions . 74 5.2.1 Informed Estimation Method: Contribution 1 . 75 5.2.2 Constraint Comparison of Informed Navigation Al- gorithm: Contributions2and3 . 81 5.3 Supplemental Validation and Observations . ... 94 5.3.1 Applied Metrics to Realistic Terrain Models . 94 5.3.2 AppliedMetricstoRealEnvironments . 111 VI CONCLUSION................................ 125 6.1 ConcludingRemarks . 125 6.2 RecommendationsforFutureWork . 126 APPENDIX A PERFORMANCE VS. ηAV G ................. 129 APPENDIX B IMPACT OF λAV G ONPERFORMANCE . 133 APPENDIX C PERFORMANCE AND RESOURCE DATA . 137 REFERENCES................................... 149 vii LIST OF TABLES 1 ASPRS and NSSDA map accuracy standards (vertical), ζ =1.96. 63 2 Error: Human expert-generated map vs. SECT-II map. 71 3 Error: Algorithm-generated map vs. SECT-II map. 71 4 Error: Human expert-generated map vs. Algorithm-generated map..................................... 72 5 Average RMS vertical accuracy of 20 map samples at three horizontalresolutionsofaccuracy. 73 6 Coverage ranges for 115 [m] x 115 [m] test area at meter resolution. 77 7 Relevant performance and resource data for navigation strate- gies lawnmower-traditional, lawnmower-random, and lawnmower- informed navigation for specific coverages presented in Figure 50. ..................................... 86 8 Coverage ranges for 20 [m] x 10 [m] test area at centimeter resolution. 95 9 Relevant performance and resource data for navigation strate- gies lawnmower-traditional, lawnmower-random, and lawnmower- informed navigation for specific coverages presented in Figure 62. ..................................... 104 10 Relevant performance and resource data for navigation strate- gies lawnmower-traditional, lawnmower-random, and lawnmower- informed navigation for specific coverages presented in Figure 63. ..................................... 108 11 Coverage ranges for 20 [m] x 40 [m] test area at centimeter resolution. 114 12 Relevant performance and resource data for navigation strate- gies lawnmower-traditional, lawnmower-random, and lawnmower- informed navigation for specific coverages presented in Figure 71. ..................................... 119 13 User-defined performance and resource constraints based on NN ASPRS Class 1 map accuracy standard and DMax, respectively. 120 14 Relevant performance and resource data for lawnmower-traditional navigation across all coverages for simulated DEMs. .... 137 15 Relevant performance and resource data for lawnmower-random navigation across all coverages for simulated DEMs. .... 138 viii 16 Relevant performance and resource data for lawnmower-informed navigation across all coverages for simulated DEMs. .... 139 17 Relevant performance and resource data for lawnmower-traditional navigation across all coverages for realistic DEMs. ..... 140 18 Relevant performance and resource data for lawnmower-random navigation across all coverages for realistic DEMs. ..... 141 19 Relevant performance and resource data for lawnmower-informed navigation across all coverages for realistic DEMs. ..... 142 20 Relevant performance and resource data for lawnmower-traditional navigation across all coverages for MODIS DEMs. 143 21 Relevant performance and resource data for lawnmower-random navigation across all coverages for MODIS DEMs. 144 22 Relevant performance and resource data for lawnmower-informed navigation across all coverages for MODIS DEMs. 145 23 Relevant performance and resource data for lawnmower-traditional navigation across all coverages for a real DEM.. 146 24 Relevant performance and resource data for lawnmower-random navigation across all coverages for a real DEM.. 147 25 Relevant performance and resource data for lawnmower-informed navigation across all coverages for a real DEM.. 148 ix LIST OF FIGURES 1 Extracted pixel-scale area of interest for a robotic survey sys- tem from a larger satellite image. Enlarged view of pixel pro- vides no spatial variability information. ... 5 2 Three example sampling patterns (from left to right, unaligned random, unaligned stratified, and aligned systematic) used in [1] for optimizing sample placement in terrain measurment....
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