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Robotic Mapping and Exploration: Springer Tracts in Advanced Robotics, cartea 55

Autor Cyrill Stachniss
en Limba Engleză Hardback – 27 apr 2009
"Robotic Mapping and Exploration" is an important contribution in the area of simultaneous localization and mapping  (SLAM) for autonomous robots, which has been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the autonomous mapping learning problem. Solutions include uncertainty-driven exploration, active loop closing, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments. Results are accompanied by a rich set of experiments, revealing a promising outlook toward the application to a wide range of mobile robots and field settings, such as search and rescue, transportation tasks, or automated vacuum cleaning.
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Specificații

ISBN-13: 9783642010965
ISBN-10: 3642010962
Pagini: 220
Ilustrații: XVIII, 198 p.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.49 kg
Ediția:2009
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Springer Tracts in Advanced Robotics

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Basic Techniques.- I: Exploration with Known Poses.- Decision-Theoretic Exploration Using Coverage Maps.- Coordinated Multi-Robot Exploration.- Multi-Robot Exploration Using Semantic Place Labels.- II: Mapping and Exploration under Pose Uncertainty.- Efficient Techniques for Rao-Blackwellized Mapping.- Actively Closing Loops During Exploration.- Recovering Particle Diversity.- Information Gain-based Exploration.- Mapping and Localization in Non-Static Environments.- Conclusion.

Textul de pe ultima copertă

"Robotic Mapping and Exploration" is an important contribution in the area of simultaneous localization and mapping  (SLAM) for autonomous robots, which has been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the autonomous mapping learning problem. Solutions include uncertainty-driven exploration, active loop closing, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments. Results are accompanied by a rich set of experiments, revealing a promising outlook toward the application to a wide range of mobile robots and field settings, such as search and rescue, transportation tasks, or automated vacuum cleaning.

Caracteristici

Recent research in the area of self-localization and mapping (SLAM) for autonomous robots The presented solutions include uncertainty-driven exploration, active loop closing, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments. accompanied by a rich set of experiments