Envío gratis a partir de 69,99 euros.

«Comprobar estado del pedido»

Entre a formar parte de una comunidad de amantes de los libros del mundo entero y acceda a un sinfín de ventajas. Crear una cuenta gratis

Envío gratuito con Zásilkovna para compras superiores a 59.99 €

Mensajería SEUR 5.49 € Mensajería GLS 8.49 € Mensajería Correos 5.99 € Mensajería DHL 5.99 € Punto SEUR 4.49 €

Contacto

Cómo comprar

Ayuda

Mi cuenta

▸ Vacío :-(

Envío gratis a partir de 69,99 euros.

State Estimation for Robotics

Name: State Estimation for Robotics
Brand: Cambridge University Press
SKU: 39903165
Price: 132.49 EUR
Availability: InStock
Author: Timothy D. Barfoot
ISBN: 9781108506731

Timothy D. Barfoot

Idioma

Inglés

Libro electrónico Adobe ePub DRM

Código Libristo: 39903165

Editores Cambridge University Press, julio 2017

A key aspect of robotics today is estimating the state, such as position and orientation, of a robot... Descripción completa

Código Libristo: 39903165

324 b

132.49 €

Entrega en Navidad ▾ En stock Descarga instantánea

Clientes que también han comprado

Ateísmo y laicidad Joan Carles Marset Caus

Tapa blanda

16.99 €

Mutterschaft Thomas Überhoff

Tapa blanda

15.99 €

DIDEROT AND THE ENCYCLOPAEDISTS. (1891). MORLEY JOHN Libro

Tapa dura

163.69 €

Lo politico en practicas esteticas juveniles Ibeth Molina

Adobe ePub DRM

3.69 €

Галопом навстречу Евгения Русинова

Tapa dura

15.49 €

Fränkische Volkslieder. Franz Wilhelm von Ditfurth

Tapa dura

37.79 €

A key aspect of robotics today is estimating the state, such as position and orientation, of a robot as it moves through the world. Most robots and autonomous vehicles depend on noisy data from sensors such as cameras or laser rangefinders to navigate in a three-dimensional world. This book presents common sensor models and practical advice on how to carry out state estimation for rotations and other state variables. It covers both classical state estimation methods such as the Kalman filter, as well as important modern topics such as batch estimation, the Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. The methods are demonstrated in the context of important applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Students and practitioners of robotics alike will find this a valuable resource.