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Automatic Construction of 3D Basic-Semantic Models of Inhabited Interiors Using Laser Scanners and RFID Sensors
School of Computer Engineering, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16. 28040 Madrid, Spain
3D Visual Computing and Robotics Lab, Universidad de Castilla-La Mancha (UCLM), Paseo de la Universidad, 4. 13071 Ciudad Real, Spain
* Author to whom correspondence should be addressed.
Received: 22 March 2012; in revised form: 16 April 2012 / Accepted: 2 May 2012 / Published: 3 May 2012
Abstract: This paper is focused on the automatic construction of 3D basic-semantic models of inhabited interiors using laser scanners with the help of RFID technologies. This is an innovative approach, in whose field scarce publications exist. The general strategy consists of carrying out a selective and sequential segmentation from the cloud of points by means of different algorithms which depend on the information that the RFID tags provide. The identification of basic elements of the scene, such as walls, floor, ceiling, windows, doors, tables, chairs and cabinets, and the positioning of their corresponding models can then be calculated. The fusion of both technologies thus allows a simplified 3D semantic indoor model to be obtained. This method has been tested in real scenes under difficult clutter and occlusion conditions, and has yielded promising results.
Keywords: 3D modeling; RFID; laser scanner; 3D data processing
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Cite This Article
MDPI and ACS Style
Valero, E.; Adan, A.; Cerrada, C. Automatic Construction of 3D Basic-Semantic Models of Inhabited Interiors Using Laser Scanners and RFID Sensors. Sensors 2012, 12, 5705-5724.
Valero E, Adan A, Cerrada C. Automatic Construction of 3D Basic-Semantic Models of Inhabited Interiors Using Laser Scanners and RFID Sensors. Sensors. 2012; 12(5):5705-5724.
Valero, Enrique; Adan, Antonio; Cerrada, Carlos. 2012. "Automatic Construction of 3D Basic-Semantic Models of Inhabited Interiors Using Laser Scanners and RFID Sensors." Sensors 12, no. 5: 5705-5724.