Probabilistic robotics

By:

Thrun, Sebastian

Contributor(s):

Material type: Text

TextPublication details: London MIT Press 2006Description: xx., 647pISBN:

9780262201629 (hb.)

Subject(s):

DDC classification:

629.892 THR

Summary: Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

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Holdings
Item type	Current library	Collection	Call number	Status	Barcode
Book	Plaksha University Library	Technology	629.892 THR (Browse shelf(Opens below))	Available	003885
Book	Plaksha University Library	Technology	629.892 THR (Browse shelf(Opens below))	Available	003630
Book	Plaksha University Library	Engineering	629.892 THR (Browse shelf(Opens below))	Available	002903

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https://mitpress.mit.edu/books/probabilistic-robotics

Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

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