000			03546nam a22004455i 4500
001			u373963
003			SIRSI
005			20160812084211.0
007			cr nn 008mamaa
008			100301s2010 gw | s |||| 0|eng d
020			_a9783642112102 _9978-3-642-11210-2
040			_cMX-MeUAM
050		4	_aTJ210.2-211.495
050		4	_aTJ163.12
082	0	4	_a629.8 _223
100	1		_aMozos, Óscar Martínez. _eauthor.
245	1	0	_aSemantic Labeling of Places with Mobile Robots _h[recurso electrónico] / _cby Óscar Martínez Mozos.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aXXI, 138 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Tracts in Advanced Robotics, _x1610-7438 ; _v61
505	0		_aSupervised Learning -- Semantic Learning of Places from Range Data -- Topological Map Extraction with Semantic Information -- Probabilistic Semantic Classification of Trajectories -- Semantic Information in Exploration and Localization -- Conceptual Spatial Representation of Indoor Environments -- Semantic Information in Sensor Data -- Conclusion.
520			_aDuring the last years there has been an increasing interest in the area of service robots. Under this category we find robots working in tasks such as elderly care, guiding, office and domestic assistance, inspection, and many more. Service robots usually work in indoor environments designed for humans, with offices and houses being some of the most typical examples. These environments are typically divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend its representation of the environment, and to improve its capabilities. As an example, natural language terms like corridor or room can be used to indicate the position of the robot in a more intuitive way when communicating with humans. This book presents several approaches to enable a mobile robot to categorize places in indoor environments. The categories are indicated by terms which represent the different regions in these environments. The objective of this work is to enable mobile robots to perceive the spatial divisions in indoor environments in a similar way as people do. This is an interesting step forward to the problem of moving the perception of robots closer to the perception of humans. Many approaches introduced in this book come from the area of pattern recognition and classification. The applied methods have been adapted to solve the specific problem of place recognition. In this regard, this work is a useful reference to students and researchers who want to introduce classification techniques to help solve similar problems in mobile robotics.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aControl, Robotics, Mechatronics.
650	2	4	_aArtificial Intelligence (incl. Robotics).
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642112096
830		0	_aSpringer Tracts in Advanced Robotics, _x1610-7438 ; _v61
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-11210-2
596			_a19
942			_cLIBRO_ELEC
999			_c201843 _d201843