DocumentCode :
3331330
Title :
Analysis of data fusion methods in certainty grids application to collision danger monitoring
Author :
Puente, E.A. ; Moreno, L. ; Salichs, M.A. ; Gachet, D.
Author_Institution :
Dept. Ingenieria de Sistemas y Autom., Univ., Politecnica de Madrid, Spain
fYear :
1991
fDate :
28 Oct-1 Nov 1991
Firstpage :
1133
Abstract :
The authors focus on the use of the occupancy grid representation to maintain and combine the information acquired from sensors about the environment. This information is subsequently used to monitor the robot collision danger risk and take into account that risk in starting the appropriate maneuver. The occupancy grid representation uses a multidimensional tessellation of space into cells, where each cell stores some information about its state. A general model associates a random vector that encodes multiple properties in a cell state. If the cell property is limited to occupancy, it is usually called occupancy grid. Two main approaches have been used to model the occupancy of a cell: probabilistic estimation and the Dempster-Shafer theory of evidence. Probabilistic estimation and some combination rules based on the Dempster-Shafter theory of evidence are analyzed and their possibilities compared
Keywords :
artificial intelligence; mobile robots; monitoring; navigation; probability; signal processing; Dempster-Shafer theory of evidence; artificial intelligence; autonomous mobile robots; certainty grids; collision danger monitoring; data fusion; multidimensional tessellation; probabilistic estimation; signal processing; Buildings; Data analysis; Fuses; Geometry; Mobile robots; Monitoring; Recursive estimation; Remotely operated vehicles; Sensor fusion; State estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-87942-688-8
Type :
conf
DOI :
10.1109/IECON.1991.239281
Filename :
239281
Link To Document :
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