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Robotic Mapping and Exploration [electronic resource] / by Cyrill Stachniss.

Por: Tipo de material: TextoTextoSeries Springer Tracts in Advanced Robotics ; 55 | Springer Tracts in Advanced Robotics ; 55Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009Descripción: XVIII, 198 p. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • online resource
ISBN:
  • 9783642010972
Trabajos contenidos:
  • SpringerLink (Online service)
Tema(s): Formatos físicos adicionales: Sin títuloClasificación CDD:
  • 629.8 23
Clasificación LoC:
  • TJ210.2-211.495
  • TJ163.12
Recursos en línea:
Contenidos:
Springer eBooksResumen: "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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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.

"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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