| Description |
1 online resource (viii, 116 pages) : illustrations |
| Series |
SpringerBriefs in computer science |
|
SpringerBriefs in computer science.
|
| Contents |
Machine generated contents note: 1. Introduction -- 1.1. Motivation -- 1.2. Classical Approaches -- 1.3. Document Organization -- References -- 2. Distributed Data Association -- 2.1. Introduction -- 2.2. Problem Description -- 2.2.1. Matching Between Two Cameras -- 2.2.2. Centralized Matching Between n Cameras -- 2.2.3. Distributed Matching Between n Cameras -- 2.3. Propagation of Local Associations -- 2.4. Algorithm Based on Trees -- 2.5. Feature Labeling -- 2.6. Algorithm Based on the Maximum Error Cut -- 2.7. Simulations -- 2.8. Closure -- References -- 3. Distributed Localization -- 3.1. Introduction -- 3.2. Problem Description -- 3.3. Planar Localization from Noisy Measurements -- 3.3.1. Centralized Algorithm -- 3.3.2. Distributed Algorithm -- 3.4. Centroid-Based Position Estimation from Noisy Measurements -- 3.4.1. Position Estimation Relative to an Anchor -- 3.4.2. Centralized Centroid-Based Position Estimation -- 3.4.3. Distributed Centroid-Based Position Estimation -- 3.5. Simulations -- 3.6. Closure -- References -- 4. Map Merging -- 4.1. Introduction -- 4.2. Problem Description -- 4.3. Dynamic Map Merging Algorithm -- 4.4. Properties of the Dynamic Map Merging Algorithm -- 4.5. Simulations -- 4.6. Closure -- References -- 5. Real Experiments -- 5.1. Data Association with Visual Data -- 5.2. Map Merging with Visual Data -- 5.3. Data Association, Localization, and Map Merging with RGB-D -- 5.4. Closure -- References -- 6. Conclusions |
| Summary |
This work examines the challenges of distributed map merging and localization in multi-robot systems, which enables robots to acquire the knowledge of their surroundings needed to carry out coordinated tasks. After identifying the main issues associated with this problem, each chapter introduces a different distributed strategy for solving them. In addition to presenting a review of distributed algorithms for perception in localization and map merging, the text also provides the reader with the necessary tools for proposing new solutions to problems of multi-robot perception, as well as other interesting topics related to multi-robot scenarios. This work will be of interest to postgraduate students and researchers in the robotics and control communities, and will appeal to anyone with a general interest in multi-robot systems. The reader will not require any prior background knowledge, other than a basic understanding of mathematics at a graduate-student level. The coverage is largely self-contained, supported by numerous explanations and demonstrations, although references for further study are also supplied |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed November 5, 2015) |
| Subject |
Robotics.
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Robots -- Control systems.
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Intelligent control systems.
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Pattern perception.
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Robotics
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Robotics.
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Network hardware.
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Image processing.
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Artificial intelligence.
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TECHNOLOGY & ENGINEERING -- Engineering (General)
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Intelligent control systems
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Pattern perception
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Robotics
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Robots -- Control systems
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| Form |
Electronic book
|
| Author |
Sagues, Carlos, author
|
|
Mezouar, Youcef, author
|
| ISBN |
9783319258867 |
|
3319258869 |
|
