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.
A growing heterogeneity of demand, the advent of ';long tail markets';, exploding product complexities, and the rise of creative consumers are challenging companies in all industries to find new strategies to address these trends. Mass customization (MC) has emerged in the last decade as the premier strategy for companies in all branches of industry to profit from heterogeneity of demand and a broad scope of other customer demands.The research and practical experience collected in this book presents the latest thinking on how to make mass customization work. More than 50 authors from academia and management debate on what is viable now, what did not work in the past, and what lurks just below the radar in mass customization, personalization, and related fields.Edited by two leading authorities in the field of mass customization, both volumes of the book discuss, among many other themes, the latest research and insights on customization strategies, product design for mass customization, virtual models, co-design toolkits, customization value measurement, open source architecture, customization communities, and MC supply chains. Through a number of detailed case studies, prominent examples of mass customization are explained and evaluated in larger context and perspective.
In recent years, the supply chain has become a key element to the survival and prosperity of organisations in different industry sectors. Organisations dealing in dynamic business environments demand supply chains that support the satisfaction of customer needs. The principles of lean thinking that once permeated standalone organisations have now been transferred to the supply chain, making imperative the development of innovative approaches to supply chain management. Customer-driven Supply Chains: Strategies for Lean and Agile Supply Chain Design reviews the concept of lean thinking and its relationship to other key initiatives associated with supply chain management. Detailed industrial case studies based on the authors’ experience illustrate the principles behind lean supply chains. Moreover, a series of diagrams are used to illustrate critical concepts and supply chain architectures. Special emphasis is placed on the importance of transferring lean principles from the organisational level to the supply chain level. The theory and principles behind lean supply chains are reviewed. Other concepts related to lean supply chains discussed in the book include: mass customisation, agility, information sharing and the bullwhip effect. A methodology used to measure the performance of supply chains is introduced; this methodology comprises the tools of decision timeline, data-flow diagramming, supply chain value stream mapping and a performance measurement scorecard. Readers will gain a clear picture of the competitive implications of lean supply chains. Customer-driven Supply Chains: Strategies for Lean and Agile Supply Chain Design will be a valuable resource of material to students studying supply chain/operations management as well as researchers in this field. Industry practitioners will learn how to develop sound supply chain strategies that can have a positive impact in their organisation.
A growing heterogeneity of demand, the advent of "long tail markets", exploding product complexities, and the rise of creative consumers are challenging companies in all industries to find new strategies to address these trends. Mass customization (MC) has emerged in the last decade as the premier strategy for companies in all branches of industry to profit from heterogeneity of demand and a broad scope of other customer demands. The research and practical experience collected in this book presents the latest thinking on how to make mass customization work. More than 50 authors from academia and management debate on what is viable now, what did not work in the past, and what lurks just below the radar in mass customization, personalization, and related fields. Edited by two leading authorities in the field of mass customization, both volumes of the book discuss, among many other themes, the latest research and insights on customization strategies, product design for mass customization, virtual models, co-design toolkits, customization value measurement, open source architecture, customization communities, and MC supply chains. Through a number of detailed case studies, prominent examples of mass customization are explained and evaluated in larger context and perspective.
My interest in microsimulation started to develop when I was exposed to the works of Guy Orcutt and his associates on microsimulation of households in the USA, and those of Gunnar Eliasson and his associates on simulatio~ of Swedish firms. Their approaches promised the exciting possibility to represent an by simulating the behaviour of individual microeconomic entire economic system units on a computer. The construction of a large scale microsimulation model seemed to be a worthwhile adventure which could yield much more detailed results than existing models. It was also evident that microsimulation of firms is a relatively underdeveloped area, in spite of the large number of operational microsimulation models of households in the USA and Europe. Developing the computer implementation has been an integral part of the research. Translating initially vague ideas into mathematical formulae and subsequently into a structured computer language provides a testing ground for 10Bical consistency of ideas. When writing this book I have purposefully abstained from describing the computer program and dedicated solution algorithms. The reason is that the book is primarily directed towards readers interested in economics and therefore uses the language of economics and not that of computer science. The simulation model has been programmed for the personal computer in Turbo Pascal. Sophisticated memory management techniques have lifted constraints on the number of firms which can be simulated on the PC.
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