The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-3/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-3/W1-2022, 255–261, 2022
https://doi.org/10.5194/isprs-archives-XLVI-3-W1-2022-255-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-3/W1-2022, 255–261, 2022
https://doi.org/10.5194/isprs-archives-XLVI-3-W1-2022-255-2022
 
22 Apr 2022
22 Apr 2022

INDOOR VISIBLE LIGHT LOCALIZATION METHOD BASED ON EMBEDDED ARTIFICIAL INTELLIGENCE

F. Zhang1, W. Ke1,2, H. Ouyang1, and S. Qiu1 F. Zhang et al.
  • 1Jiangsu Key Laboratory on Opto-Electronic Technology, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing 210023, China
  • 2Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023,China

Keywords: Visible Light Positioning, Image Classification Algorithm, Embedded Artificial Intelligence

Abstract. This paper proposes an indoor visible light location method based on embedded platform and optical frequency image recognition technology with artificial intelligence, which can effectively improve the location effect in complex indoor environment. By transplanting the artificial intelligence (AI) based image classification algorithm into the embedded platform, this method uses a forward neural network to analyse the position information coming from the coded optical frequency image received by a camera, and then the positioning results can be obtained. In view of the "motion state" and "occlusion state" that are most likely to fail in traditional visible image localization, we specially supplement the training set of relevant characteristic optical frequency images to enhance the robustness of the method. According to the track and positioning results of the moving platform receiver, the proposed method can provide accurate and reliable navigation and positioning and has stronger anti-interference ability compared with the traditional light intensity or light image positioning methods.