Research on a Novel Indoor Localization Method for Mobile Robot

2011 ◽  
Vol 103 ◽  
pp. 119-123
Author(s):  
Yuan Luo ◽  
Wei Xi Kong ◽  
Yi Zhang ◽  
Ti Wei Wei
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Image Sequences ◽  
Current View ◽  
Feature Points ◽  
Complex Environment ◽  
Localization Method ◽  
Natural Feature ◽  
Geometrical Relationship

In this paper, a novel indoor localization method for mobile robot working in complex environment is presented. Natural features are obtained from 3D rebuilding image sequences. The geometrical relationship between the natural feature points and the homologous points of current view are found to locate the positions of a moving robot. Experiment result shows that this novel localization method is reliable and effective.

Vision for mobile robots

1992 ◽  
Vol 337 (1281) ◽  
pp. 341-350 ◽  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Structure From Motion ◽  
Optic Flow ◽  
Image Sequences ◽  
Additional Constraint ◽  
Feature Points ◽  
Scene Structure ◽  
Corner Points ◽  
Edge Points

Localized feature points, particularly corners, can be computed rapidly and reliably in images, and they are stable over image sequences. Corner points provide more constraint than edge points, and this additional constraint can be propagated effectively from corners along edges. Implemented algorithms are described to compute optic flow and to determine scene structure for a mobile robot using stereo or structure from motion. It is argued that a mobile robot may not need to compute depth explicitly in order to navigate effectively.

Using RSSI Simple Localization Method to Implement the Context-Aware and Social Recommendation System

2013 ◽  
Vol 479-480 ◽  
pp. 1213-1217
Author(s):  
Mu Yen Chen ◽  
Ming Ni Wu ◽  
Hsien En Lin
Keyword(s):  
Social Media ◽  
Social Relations ◽  
Context Awareness ◽  
Recommendation System ◽  
Product Information ◽  
Indoor Localization ◽  
Context Aware ◽  
Social Recommendation ◽  
Localization Method ◽  
The Social

This study integrates the concept of context-awareness with association algorithms and social media to establish the Context-aware and Social Recommendation System (CASRS). The Simple RSSI Indoor Localization Module (SRILM) locates the user position; integrating SRILM with Apriori Recommendation Module (ARM) provides effective recommended product information. The Social Media Recommendation Module (SMRM) connects to users social relations, so that the effectiveness for users to gain product information is greatly enhanced. This study develops the system based on actual context.

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