Intelligent Mapping for Autonomous Robotic Survey David R. Thompson Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania 15213 Submitted in partial fullfilment of the requirements for the degree of Doctor of Philosophy Thesis Committee: David Wettergreen, Chair Reid Simmons Jeff Schneider Steve Chien, Jet Propulsion Laboratory Phil Christensen, Arizona State University c David Thompson I Acknowledgments [Thank Francisco Calder´on, Dominic Jonak, James Teza, David Wettergreen, ASU Geol- ogists, Friends, Family, etc. ] III Abstract In general today’s planetary exploration robots cannot travel beyond the previous day’s imagery. Rovers remain in the same location for extended periods and perform dense sampling of each locale. However, advances in autonomous navigation will soon permit traverses of multiple kilometers. This promises significant benefits for planetary science: rovers can visit multiple sites per command cycle and survey vast tracts of terrain. Long traverses also present new challenges. These rovers will travel over their local horizon so that scientists will not be able to specify targets in advance. Moreover, energy and time shortages will continue to limit the number of measurements so that sampling density will decrease as mobility improves. Finally, constraints on communications band- width will preclude transmitting most of the collected data. These issues raise the ques- tion: is it possible to explore efficiently, with long traverses and sparse sampling, without sacrificing our understanding of the visited terrain? “Science autonomy” addresses the optimal sampling problem through onboard data understanding. Pattern recognition, learning and planning technologies can enable robots to place instruments and take measurements without human supervision. These robots can autonomously choose the most important features to observe and transmit. This document argues that these agents should learn and exploit structure in the ex- plored environment. In other words, they must be mapmakers. We advocate the use of intelligent maps — onboard predictive models that represent spatial structure (similari- ties from one locale to the next) and cross-sensor structure (correlations between different sensing scales). These models guide the agent’s exploration to informative areas while minimizing redundant sampling. The generative model allows us to formulate the exploration problem in terms of es- tablished principles of experimental design. Spatial experimental design criteria guide exploration decisions and suggest the best data products for downlink. In addition the map itself functions as a bandwidth-efficient representation of data gathered during the traverse. The proposed work bridges the gap between Bayesian experimental design, robotic mapping and their application in autonomous rover geology. We develop generative data models that are appropriate for geologic mapping and site survey by planetary rovers. We present algorithms for learning map parameters on the fly and leveraging these contextual cues to choose optimal data collection and return actions. Finally we implement and test adaptive exploration schemes for kilometer-scale site survey tasks. Contents 1 Introduction 1 1.1 TheRemoteExplorationProblem . 2 1.2 IntelligentMapsforSiteSurvey . .. 5 1.3 Contributions ................................ 8 1.4 ThesisOverview .............................. 9 2 Feature Detection and Classification 13 2.1 Previous Work in Geologic Feature Detection and Classification .. 14 2.1.1 PreviousWorkinGeologicFeatureDetection . ... 14 2.1.2 PreviousworkinFeatureClassification . .. 20 2.2 FeatureDetection .............................. 22 2.2.1 FeatureDetectionApproach . 24 2.2.2 FeatureDetectionPerformance. 28 2.3 FeatureTrackingandInstrumentDeployment . ..... 34 2.3.1 RoverHardware .......................... 36 2.3.2 FeatureTrackingApproach . 36 2.3.3 VisualServo ............................ 43 2.3.4 FeatureTrackingPerformance . 45 3 Spatial Models 53 3.1 Previous work in Mapmaking for Robotic Exploration . ....... 53 3.2 GaussianProcessBackground . 56 3.2.1 InferencewithCovarianceFunctions . 58 3.2.2 LearningHyperparameters . 61 3.3 StationaryandNonstationaryEnvironments . ...... 62 3.3.1 NonstationaryCovarianceFunctions . .. 63 3.3.2 AugmentedInputSpaces. 66 3.3.3 EvaluationonSyntheticData. 68 V VI CONTENTS 3.4 FieldTests.................................. 71 3.4.1 TheAmboyCraterLavaField . 72 3.4.2 RoverInstruments ......................... 75 3.4.3 Procedure.............................. 77 3.4.4 Results ............................... 82 4 Adaptive Data Collection 87 4.1 ObjectiveFunctionsforRemoteExploration . ...... 88 4.1.1 CoupledandDecoupledSolutions . 89 4.2 PreviousWorkinAdaptiveDataCollection . .... 90 4.2.1 PeriodicSampling ......................... 92 4.2.2 TargetSignatures .. .. .. .. .. .. .. 93 4.2.3 Optimizing Data Collection for Target Signatures . ...... 96 4.2.4 RepresentativeSampling . 97 4.2.5 NoveltyDetection ......................... 98 4.2.6 LearningMixedRewardCriteria. 98 4.2.7 Information-DrivenRewardFunctions . .. 99 4.2.8 SpatialDesign ........................... 102 4.2.9 Discussion ............................. 105 4.3 AdaptiveSamplingfromaTransect . 108 4.3.1 Data Collectionfrom an AccessibleEnvironment . .... 109 4.3.2 DataCollectionfromaTemporalSeries . 113 4.4 TheCorridorExplorationTask . 115 4.4.1 ProblemFormulation. 115 4.4.2 Software Architecture and Execution Strategy . ..... 118 4.4.3 FieldTestProcedure . 119 4.4.4 FieldTestResults. 121 5 Selective Transmission with Proxy Features 127 5.1 Proxy Features as Covariates in a Regression Model . ....... 130 5.1.1 SharedHyperparameterLearning . 131 5.1.2 SelectiveReturnofImageSequences . 137 5.2 ProxyFeaturesasNoisyObservations . 144 5.3 BoundarydetectionwithHMMs . 148 5.3.1 Discussion ............................. 152 CONTENTS VII 6 Conclusions 155 6.1 Contributions ................................ 155 6.2 FutureWork................................. 156 6.3 MapsandExploration. .. .. .. .. .. .. .. 157 Chapter 1 Introduction Since nature is too complex and varied to be explored at random, [the] map is as essential as observation and experiment to science’s continuing develop- ment. - T. S. Kuhn, The Structure of Scientific Revolutions [1] Maps are as old as exploration. For each culture that ventures beyond the safety of the vil- lage we find some distinctive new map: an assembly of sticks to represent ocean currents, a trail portrayed by scratches in bark, or a coastline sculpted in miniature [2]. Today the frontiers of exploration are the planetary bodies of our solar system. Much of what we know and theorize about them is best expressed by maps. Maps are important to planetary scientists because they reveal the underlying structure of disparate observations. Figure 1.1 (left) illustrates this role. Here a geologic map shows several square kilometers of the Gusev crater region on Mars, identifying spatial trends and boundaries that indicate formative processes [3]. The map identifies regions of homo- geneity and change, suggesting areas that warrant further exploration. The science map of Figure 1.1 (right) shows individual rocks within 10m of the Pathfinder mission land- ing site [4]. Its scale is radically different but its function is similar: to represent spatial relationships among simple features that reveal larger patterns and imply richer scientific interpretations. Mapmaking, as the discovery and representation of structure in spatial data, is a vital tool for scientists and an apt metaphor for scientific exploration in general [1, 2]. Scientists seeking to understand a remote environment do not evaluate individual features in isolation but instead build a holistic account of many measurements and their distribution in space. The map embodies this process by situating each new observation in its relationship to the whole. Its coverage establishes the area of exploration, and the features themselves define the phenomena and interrelationships that the analysis will consider [5]. 1 2 CHAPTER 1. INTRODUCTION Figure 1.1: Left: A portion of the Gusev crater region on Mars. The colors and markings in this geologic map indicate predicted units of different surface material. Image cour- tesy USGS [3]. Right: Rocks at the Pathfinder landing site. Circle diameters indicate rock sizes. Shaded areas are unobserved due to occlusion. Image from Haldemann and Golombek [4]. This document demonstrates that autonomous explorer agents can also be mapmakers. Explorer robots performing site survey face significant resource constraints; they must characterize large remote environments using very few observations. Onboard science maps form a framework by which these agents can learn, represent and exploit the structure of their environment to explore more efficiently. Mapmaker agents can leverage trends in collected data to guide navigation, identify informative features for measurement, and minimize redundant science observations. 1.1 The Remote Exploration Problem The investigation into autonomous mapmaking is motivated by continuing improvements in spacecraft lifespan and mobility. The recent history of Mars exploration exemplifies this trend (Figure 1.2). The immobile Viking lander preceded the 1997 Pathfinder mission that could travel within the field of view of the landing craft. By 2003 the Mars Exploration Rovers could travel tens of meters per day. The Mars Science Laboratory will build on these improvements with power and size to travel even greater