We review the use of factor graphs for the modeling and solving of large-scale inference problems in robotics. Factor graphs are a family of probabilistic graphical models, other examples of which are Bayesian networks and Markov random fields, well known from the statistical modeling and machine learning literature. They provide a powerful abstraction that gives insight into particular inference problems, making it easier to think about and design solutions, and write modular software to perform the actual inference. We illustrate their use in the simultaneous localization and mapping problem and other important problems associated with deploying robots in the real world. We introduce factor graphs as an economical representation within which to formulate the different inference problems, setting the stage for the subsequent sections on practical methods to solve them. We explain the nonlinear optimization techniques for solving arbitrary nonlinear factor graphs, which requires repeatedly solving large sparse linear systems. The sparse structure of the factor graph is the key to understanding this more general algorithm, and hence also understanding (and improving) sparse factorization methods. We provide insight into the graphs underlying robotics inference, and how their sparsity is affected by the implementation choices we make, crucial for achieving highly performant algorithms. As many inference problems in robotics are incremental, we also discuss the iSAM class of algorithms that can reuse previous computations, re-interpreting incremental matrix factorization methods as operations on graphical models, introducing the Bayes tree in the process. Because in most practical situations we will have to deal with 3D rotations and other nonlinear manifolds, we also introduce the more sophisticated machinery to perform optimization on nonlinear manifolds. Finally, we provide an overview of applications of factor graphs for robot perception, showing the broad impact factor graphs had in robot perception.
The quest to comprehend the essence of human nature is as old as the capacity for reflective thought. In this provocative book, Dr. Michael Robbins proposes a new approach that draws upon psychoanalysis but is shaped by awareness of the limits that the particular circumstances of historical epoch, Western culture, male gender, and modal population from which psychoanalysis was derived impose on its modernist claims to being a universal theory. Dr. Robbins addresses these limitations from the perspective of philosophy of science, focusing on the paradigm shift from logical positivism, which seeks to reduce complexity and diversity to its presumptive causal building blocks, to the postmodern emphasis on pluralism and on relativistic, contextual, evanescent knowledge. He examines the implications of this shift for the disciplines that study human nature-neuroscience, psychoanalysis, gender studies, anthropology, and sociology. After considering whether typical personality has changed over historical time and studying the cross-cultural diversity of human nature, the relationship of gender to personality, the spectrum of personality variability within Western culture, and the relationship of the contextual embeddedness of the conceiver to his or her theory, he proposes a dialectical conception of personality based on systems and chaos theories that respects its multiple guises and circumstantial richness of content without abandoning the quest for universal principles of organization and development.
From the physician behind the wildly popular NutritionFacts website, How Not to Die reveals the groundbreaking scientific evidence behind the only diet that can prevent and reverse many of the causes of disease-related death. The vast majority of premature deaths can be prevented through simple changes in diet and lifestyle. In How Not to Die, Dr. Michael Greger, the internationally-renowned nutrition expert, physician, and founder of NutritionFacts.org, examines the fifteen top causes of premature death in America-heart disease, various cancers, diabetes, Parkinson's, high blood pressure, and more-and explains how nutritional and lifestyle interventions can sometimes trump prescription pills and other pharmaceutical and surgical approaches, freeing us to live healthier lives. The simple truth is that most doctors are good at treating acute illnesses but bad at preventing chronic disease. The fifteen leading causes of death claim the lives of 1.6 million Americans annually. This doesn't have to be the case. By following Dr. Greger's advice, all of it backed up by strong scientific evidence, you will learn which foods to eat and which lifestyle changes to make to live longer. History of prostate cancer in your family? Put down that glass of milk and add flaxseed to your diet whenever you can. Have high blood pressure? Hibiscus tea can work better than a leading hypertensive drug-and without the side effects. Fighting off liver disease? Drinking coffee can reduce liver inflammation. Battling breast cancer? Consuming soy is associated with prolonged survival. Worried about heart disease (the number 1 killer in the United States)? Switch to a whole-food, plant-based diet, which has been repeatedly shown not just to prevent the disease but often stop it in its tracks. In addition to showing what to eat to help treat the top fifteen causes of death, How Not to Die includes Dr. Greger's Daily Dozen -a checklist of the twelve foods we should consume every day.Full of practical, actionable advice and surprising, cutting edge nutritional science, these doctor's orders are just what we need to live longer, healthier lives.
Now in its third edition, this textbook is a comprehensive introduction to the multidisciplinary field of mobile robotics, which lies at the intersection of artificial intelligence, computational vision, and traditional robotics. Written for advanced undergraduates and graduate students in computer science and engineering, the book covers algorithms for a range of strategies for locomotion, sensing, and reasoning. The new edition includes recent advances in robotics and intelligent machines, including coverage of human-robot interaction, robot ethics, and the application of advanced AI techniques to end-to-end robot control and specific computational tasks. This book also provides support for a number of algorithms using ROS 2, and includes a review of critical mathematical material and an extensive list of sample problems. Researchers as well as students in the field of mobile robotics will appreciate this comprehensive treatment of state-of-the-art methods and key technologies.
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