| Description |
1 online resource (xii, 234 pages) |
| Series |
Springer Tracts in Advanced Robotics, 1610-7438 ; 91 |
|
Springer tracts in advanced robotics ; 91.
|
| Contents |
Probabilistic Modelling for Robotic Perception -- Fundamentals of Bayesian Inference -- Representation of 3D Space and Sensor Modelling Within a Probabilistic Framework -- Bayesian Programming and Modelling -- Hierarchical Combination of Bayesian Models and Representations -- Bayesian Decision Theory and the Action-Perception Loop -- Probabilistic Learning -- Probabilistic Approaches for Robotic Perception in Practice -- Case-Study: Bayesian 3D Independent Motion Segmentation with IMU-aided RBG-D Sensor -- Case-Study: Bayesian Hierarchy for Active Perception -- Wrapping Things Up .. |
| Summary |
This book tries to address the following questions: How should the uncertainty and incompleteness inherent to sensing the environment be represented and modelled in a way that will increase the autonomy of a robot? How should a robotic system perceive, infer, decide and act efficiently? These are two of the challenging questions robotics community and robotic researchers have been facing. The development of robotic domain by the 1980s spurred the convergence of automation to autonomy, and the field of robotics has consequently converged towards the field of artificial intelligence (AI). Since the end of that decade, the general public s imagination has been stimulated by high expectations on autonomy, where AI and robotics try to solve difficult cognitive problems through algorithms developed from either philosophical and anthropological conjectures or incomplete notions of cognitive reasoning. Many of these developments do not unveil even a few of the processes through which biological organisms solve these same problems with little energy and computing resources. The tangible results of this research tendency were many robotic devices demonstrating good performance, but only under well-defined and constrained environments. The adaptability to different and more complex scenarios was very limited. In this book, the application of Bayesian models and approaches are described in order to develop artificial cognitive systems that carry out complex tasks in real world environments, spurring the design of autonomous, intelligent and adaptive artificial systems, inherently dealing with uncertainty and the irreducible incompleteness of models |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed September 3, 2013) |
| Subject |
Robot vision.
|
|
Ingénierie.
|
|
Robot vision
|
| Form |
Electronic book
|
| Author |
Miranda Dias, Jorge, author
|
| ISBN |
9783319020068 |
|
3319020064 |
|
3319020056 |
|
9783319020051 |
|
