227,15 €
252,39 €
-10% with code: EXTRA
Robotic Mapping and Exploration
Robotic Mapping and Exploration
227,15
252,39 €
  • We will send in 10–14 business days.
"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 envi…
252.39
  • Publisher:
  • ISBN-10: 3642101682
  • ISBN-13: 9783642101687
  • Format: 15.6 x 23.4 x 1.2 cm, minkšti viršeliai
  • Language: English
  • SAVE -10% with code: EXTRA

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"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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  • Author: Cyrill Stachniss
  • Publisher:
  • ISBN-10: 3642101682
  • ISBN-13: 9783642101687
  • Format: 15.6 x 23.4 x 1.2 cm, minkšti viršeliai
  • Language: English English

"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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