2P1-H17 Position-and-pose Estimation of a Mobile Robot on a Lattice of RFID Tags

2008 ◽  
Vol 2008 (0) ◽  
pp. _2P1-H17_1-_2P1-H17_4
Author(s):  
Kenri KODAKA ◽  
Haruhiko NIWA ◽  
Yoshihiro SAKAMOTO ◽  
Yuki KANEMORI ◽  
Shigeki SUGANO
Author(s):  
K. Kodaka ◽  
H. Niwa ◽  
Y. Sakamoto ◽  
M. Otake ◽  
Y. Kanemori ◽  
...  

Author(s):  
Annalisa Milella ◽  
Paolo Vanadia ◽  
Grazia Cicirelli ◽  
Arcangelo Distante

In this paper, the use of passive Radio Frequency Identification (RFID) as a support technology for mobile robot navigation and environment mapping is investigated. A novel method for localizing passive RFID tags in a geometric map of the environment using fuzzy logic is, first, described. Then, it is shown how a mobile robot equipped with RF antennas, RF reader, and a laser range finder can use such map for localization and path planning. Experimental results from tests performed in our institute suggest that the proposed approach is accurate in mapping RFID tags and can be effectively used for vehicle navigation in indoor environments.


2012 ◽  
Vol 190-191 ◽  
pp. 651-655
Author(s):  
Qing Yang ◽  
Hong Yi Wang ◽  
Jian Cheng Li ◽  
Rong Jun Shen

RFID technology has been widely used in mobile robot positioning system for its unique advantages. RFID tags store their unique positions which are placed on the ceiling or the floor. The mobile robot carries a RFID reader which reads the RFID tags to position itself. In this paper, a new method for mobile robot localization is proposed, and the equations to calculate the position of the mobile robot are given. Finally, the experiment results show that compared to conventional positioning method, the proposed method can effectively improve the positioning accuracy of the mobile robot.


2003 ◽  
Vol 15 (3) ◽  
pp. 293-303
Author(s):  
Haiquan Yang ◽  
◽  
Nobuyuki Kita ◽  
Yasuyo Kita

A method is proposed to correct the initial position and pose estimates of a camera-head by aligning a 3D model of its surrounding environment with an observed 2D image that is captured by a foveated wideangle lens in the camera. Because of the wide field of view of the lens, the algorithm can converge even when the initial error is large, and the precision of the result is high since the resolution of the fovea of the lens is high.


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