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Indoor positioning algorithm based on improved convolutional neural network

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Abstract

Traditional navigation systems rely mainly on satellite navigation, but internal positioning and navigation cannot be achieved. This is mainly due to the inability to penetrate the wall due to the complex internal environment and the signal reaching the ground. Therefore, this paper proposes the research of indoor positioning algorithm based on improved convolutional neural network. The main method of this paper is based on the monocular vision indoor positioning based on the improved convolutional neural network. This method is an intelligent solution to the problems of traditional methods such as high cost, poor anti-interference ability, weak robustness, and poor compatibility. Positioning aids. The traditional computer vision camera pose estimation method is affected by the complex background in the image. When extracting feature corners, it is very affected by non-interest corners, so this paper adds an improved convolutional neural network algorithm to add in complex indoor scenes. With the region limitation, camera pose estimation in the region of interest is better to achieve low-cost, high accuracy and more stability. The improved model in this paper was tested on a test set of indoor datasets with markers, and the recognition accuracy reached 98.1%. In addition, this paper improves the PnP solution method, compares and analyzes the traditional RPnP, EPnP, and CEPPnP algorithms, and seeks the optimal camera pose estimation algorithm to locate the world coordinates of the current shooting position. The experimental results show that this paper proposes the positioning algorithm has high stability.

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Acknowledgements

This work was financially supported by National Major Special Science and Technology (NO.GFZX0301040115), the National Natural Science Foundation of China (No. 61301094,No.61771150 and No. 61571188), the Construct Program of the Key Discipline in Hunan Province, China, the Aid program for Science and Technology Innovative Research Team in Higher Educational Institute of Hunan Province, and the Scientific Research Project of Hunan Provincial Department of Education(No.18B458, No.18C0896 and No.19C0968); This research was financially supported in part by Key Scientific Research Projects of Hainan Education Department under Grant Hnky2017ZD-20 and Key Research Development Project of Hainan under Grant ZDYF2018234.

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Authors and Affiliations

  1. School of Information, Science and Technology, Hunan University of Humanities, Loudi, 417000, Hunan, China

    Taoyun Zhou

  2. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Taoyun Zhou, Baowang Lian & Yi Zhang

  3. School of Science, Qiongtai Normal University, Haikou, 571100, Hainan, China

    Junhua Ku

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  1. Taoyun Zhou
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  4. Yi Zhang
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Correspondence to Junhua Ku.

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Zhou, T., Ku, J., Lian, B. et al. Indoor positioning algorithm based on improved convolutional neural network. Neural Comput & Applic 34, 6787–6798 (2022). https://doi.org/10.1007/s00521-021-06112-5

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