Visible light based high accuracy indoor localization using the extinction ratio distributions of light signals

2013 ◽  
Vol 55 (6) ◽  
pp. 1385-1389 ◽  
Author(s):  
Se-Hoon Yang ◽  
Deok-Rae Kim ◽  
Hyun-Seung Kim ◽  
Yong-Hwan Son ◽  
Sang-Kook Han
2020 ◽  
Vol 12 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Weipeng Guan ◽  
Shihuan Chen ◽  
Shangsheng Wen ◽  
Zequn Tan ◽  
Hongzhan Song ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xianmin Li ◽  
Zihong Yan ◽  
Linyi Huang ◽  
Shihuan Chen ◽  
Manxi Liu

For mobile robots and location-based services, precise and real-time positioning is one of the most basic capability, and low-cost positioning solutions are increasingly in demand and have broad market potential. In this paper, we innovatively design a high-accuracy and real-time indoor localization system based on visible light positioning (VLP) and mobile robot. First of all, we design smart LED lamps with VLC and Bluetooth control functions for positioning. The design of LED lamps includes hardware design and Bluetooth control. Furthermore, founded on the loose coupling characteristics of ROS (Robot Operator System), we design a VLP-based robot system with VLP information transmitted by designed LED, dynamic tracking algorithm of high robustness, LED-ID recognition algorithm, and triple-light positioning algorithm. We implemented the VLP-based robot positioning system on ROS in an office equipped with the designed LED lamps, which can realize cm-level positioning accuracy of 3.231 cm and support the moving speed up to 20 km/h approximately. This paper pushes forward the development of VLP application in indoor robots, showing the great potential of VLP for indoor robot positioning.


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.


Optik ◽  
2021 ◽  
pp. 166853
Author(s):  
Yong Chen ◽  
Zimiao Ren ◽  
Zhaozhong Han ◽  
Huanlin Liu ◽  
Qi-xiang Shen ◽  
...  

2014 ◽  
Vol 32 (14) ◽  
pp. 2480-2485 ◽  
Author(s):  
Se-Hoon Yang ◽  
Hyun-Seung Kim ◽  
Yong-Hwan Son ◽  
Sang-Kook Han

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