1 Intelligent Mapping for Autonomous Robotic Survey David R. Thompson CMU-RI-TR-08-33 Submitted in partial fullfilment of the requirements for the degree of Doctor of Philosophy in Robotics Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania 15213 Thesis Committee: David Wettergreen, Chair Reid Simmons Jeff Schneider Steve Chien, Jet Propulsion Laboratory Phil Christensen, Arizona State University c David Thompson I Acknowledgments This thesis was only possible through guidance and support of countless others. Thanks are due to all of my friends at Carnegie Mellon who helped out with field experiments. Dom Jonak’s knowledge of the rover, its hardware and software systems proved vital to our effort. Likewise, Francisco Calder´on supplied his gifted intuition for hardware issues and a tireless work ethic. Even more than their technical abilities, their steadfast, easy-going companionship brightened our field expedition. James Teza lent a great deal of his personal time and effort in preparations for our field tests. Our experiments relied on the expert knowledge of a group of Geologists at Arizona State Uni- versity. Phil Christensen, Ron Greeley, and Shelby Cave were all vital as consultants in choosing and evaluating our field site. They humored us through a long site selection process and tolerated the eccentric requirements of the rover experiments. They supplied us with materials, advice, and technical information about multiple sites in the American Southwest. I feel strongly that in the future, the success of the autonomous science endeavor will depend on these close collaborations between researchers from the two sides of algorithm development and science applications. The Bureau of Land Management (California Needles office) went through a great deal of trouble on our behalf to facilitate an expedited permit for work at our field site. My thanks go out to Rodney Mouton and his staff. Thanks to Reid Simmons and Jeff Schneider, the committee members at Carnegie Mellon with whom I consulted throughout the past year. Their wisdom was vital to the progress of the thesis. Despite their busy schedules, they always found time to chat about my work, answer my deluge of questions, or provide advice. Staff at the Caltech Jet Propulsion Laboratory offered lots of help. They were important, both as a source of new ideas and validation techniques, but also as a check to ensure that my work remained relevant to flight projects. Steve Chien has been extremely helpful and supportive in this regard. Many of the rock detection experiments were conducted under the supervision and support of Rebecca Casta˜no from the Machine Learning and Instrument Autonomy Group. My work with proxy features paired us with scientist consultant Bob Anderson. Kiri Wagstaff was a valuable sounding board for discussing science autonomy ideas. Thanks to the other students at the Robotics Institute. Trey Smith was an invaluable mentor dur- ing my first few years at the institute. He taught me much of what I know about the research method — both the methodology, and the daily practice — by his example. My officemates Nathaniel Fair- field and Gil Jones were a great source of companionship during the thesis-writing process. The benevolent mastermind behind the curtain is certainly my advisor, David Wettergreen. Throughout my studies and this thesis he has selflessly devoted himself to my success as a stu- dent and my development as a researcher. He’s kept me challenged with new problems and well- supplied with the tools to solve them. I greatly admire David’s vision for robotics and feel lucky to have served under his watch. Finally, I owe a great gratitude to my family. My parents have been a sturdy support through the best and the worst episodes. It wouldn’t have happened without them. II This work was made possible through funding provided by a NASA ASTEP grant NNG0- 4GB66G, “Science on the Fly,” and a JPL/Caltech Strategic University Partnership Grant. III Abstract In general today’s planetary exploration robots do not travel beyond the previous day’s imagery. However, advances in autonomous navigation will soon permit traverses of multiple kilometers. These long traverses present new challenges to science-driven exploration. 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 bandwidth will preclude transmitting most of the collected data. These issues raise the question: 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 under- standing. Pattern recognition, learning and planning technologies can enable robots to place instru- ments 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 explored envi- ronment. In other words, they must be mapmakers. We advocate an intelligent mapping approach in which onboard predictive models represent spatial structure (similarities 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 established principles of experimental design. Spatial experimental design criteria guide exploration deci- sions 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. This work bridges the gap between Bayesian experimental design, robotic mapping and their application in autonomous surficial geology. We develop generative data models that are appropri- ate 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 The Remote Exploration Problem . ..... 2 1.2 Intelligent Mapping for Site Survey . ........ 5 1.3 Contributions ................................... 8 1.4 ThesisOverview .................................. 9 2 Feature Detection and Classification 11 2.1 Previous Work in Geologic Feature Detection and Classification . 12 2.1.1 Previous Work in Geologic Feature Detection . ........ 13 2.1.2 Previous work in Feature Classification . ....... 17 2.2 FeatureDetection ................................ 19 2.2.1 Feature Detection Approach . 20 2.2.2 Feature Detection Performance . ..... 25 2.3 Feature Tracking and Instrument Deployment . .......... 31 2.3.1 RoverHardware ............................... 32 2.3.2 Feature Tracking Approach . 33 2.3.3 VisualServo ................................. 40 2.3.4 Feature Tracking Performance . 42 3 Spatial Models 49 3.1 Previous work in Mapmaking for Robotic Exploration . ............ 50 3.2 Gaussian Process Background . ..... 52 3.2.1 Inference with Covariance Functions . ...... 55 3.2.2 Learning Hyperparameters . 57 3.3 Stationary and Nonstationary Environments . ........... 58 3.3.1 Nonstationary Covariance Functions . ....... 59 3.3.2 AugmentedInputSpaces. 61 3.3.3 Evaluation on Synthetic Data . 64 3.4 FieldTests...................................... 67 3.4.1 TheAmboyCraterLavaField . 68 V VI CONTENTS 3.4.2 RoverInstruments .............................. 71 3.4.3 Procedure................................... 72 3.4.4 Results .................................... 73 4 Adaptive Data Collection 83 4.1 Objective Functions for Remote Exploration . ........... 84 4.1.1 Coupled and Decoupled Solutions . 85 4.2 Previous Work in Adaptive Data Collection . ......... 87 4.2.1 PeriodicSampling .............................. 88 4.2.2 TargetSignatures .. ...... ..... ...... ..... ...... 89 4.2.3 Action Selection with Target Signatures . ........ 91 4.2.4 Representative Sampling . 93 4.2.5 NoveltyDetection .............................. 93 4.2.6 Learning Mixed Reward Criteria . 94 4.2.7 Information-Driven Reward Functions . ....... 95 4.2.8 SpatialDesign ................................ 98 4.2.9 Discussion ..................................100 4.3 Adaptive Sampling from a Transect . 102 4.4 The Corridor Exploration Task . 107 4.4.1 ProblemFormulation. 109 4.4.2 Software Architecture and Execution Strategy . ..........115 4.4.3 FieldTestProcedure . 116 4.4.4 FieldTestResults.. ...... ..... ...... ..... ...... 119 5 Selective Transmission with Proxy Features 125 5.1 Proxy Features as Covariates in a Regression Model . ............128 5.1.1 Shared Hyperparameter Learning . 129 5.1.2 Selective Return of Image Sequences . 134 5.2 Proxy Features as Noisy Observations . ........142 5.3 Boundary detection with HMMs . 146 6 Conclusions 153 6.1 Contributions ................................... 153 6.2 FutureWork...................................... 154 6.3 MapsandExploration.... ...... ..... ...... ..... .... 156 Chapter 1 Introduction Since nature is too complex and varied to be explored at random, [the] map is as essential as observation