Barfoot Timothy (38 results)

Hardcover. Condition: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Condition: New. 2. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped.

Hardcover. Condition: Like New. First Edition. A firm and square hardback with sharp corners and strong joints, just showing a few very minor rubs. Hence a non-text page has a small 'damaged' stamp. Despite such this book is actually in nearly new condition. Thus the contents are crisp, fresh and tight. Also, no pen-marks and not from a library so no such stamps or labels. Now offered for sale at a very sensible price.…

Condition: New.

Hardcover. Condition: New. Book in New condition.

Condition: As New. Unread book in perfect condition.

Condition: New.

Hardback. Condition: New. A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.…

Condition: New. 2024. 2nd ed. hardcover. . . . . .

Condition: New.

Condition: New. In English.

Condition: As New. Unread book in perfect condition.

Hardback. Condition: New. A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.…

Condition: New. 2024. 2nd ed. hardcover. . . . . . Books ship from the US and Ireland.

Hardback. Condition: New. A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.…

Condition: New. pp. 350.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.…

Hardcover. Condition: Brand New. 350 pages. 10.50x7.50x0.75 inches. In Stock.

Condition: New. In English.

Hardback. Condition: New. A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This bookcontains the proceedings of the 10th FSR, (Field and Service Robotics) which isthe leading single-track conference on applications of robotics in challengingenvironments. The 10th FSR was held in Toronto, Canada from 23-26June 2015. The book contains 42full-length, peer-reviewed papers organized into a variety of topics: Aquatic, Vision, Planetary, Aerial,Underground, and Systems.The goal ofthe book and the conference is to report and encourage the development andexperimental evaluation of field and service robots, and to generate a vibrantexchange and discussion in the community.Field robots are non-factory robots, typically mobile, that operate incomplex and dynamic environments: on theground (Earth or other planets), under the ground, underwater, in the air or inspace. Service robots are those thatwork closely with humans to help them with their lives. The first FSR was held in Canberra,Australia, in1997. Since that firstmeeting, FSR has been held roughly every two years, cycling through Asia,Americas, Europe.…

Condition: New. pp. 653.

Hardcover. Condition: Brand New. 653 pages. 9.50x6.50x1.50 inches. In Stock.

Hardcover. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Condition: new. Questo è un articolo print on demand.

Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Hardcover. Condition: new. Hardcover. A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus. This book is intended for students and practitioners of robotics working with noisy sensor data to estimate state variables. New edition highlights include a new chapter on variational inference and new sections on adaptive covariance estimation and on inertial navigation as well as a primer on matrix calculus. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …