Research Article
Indoor Visual Positioning Based on Image Retrieval in Dense Connected Convolutional Network
@INPROCEEDINGS{10.1007/978-3-030-69066-3_7, author={Xiaomeng Guo and Danyang Qin and Yan Yang}, title={Indoor Visual Positioning Based on Image Retrieval in Dense Connected Convolutional Network}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={DenseNet Feature extraction Image retrieval Indoor positioning}, doi={10.1007/978-3-030-69066-3_7} }- Xiaomeng Guo
Danyang Qin
Yan Yang
Year: 2021
Indoor Visual Positioning Based on Image Retrieval in Dense Connected Convolutional Network
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_7
Abstract
As now available methods or systems based on image retrieval and visual researchs are implemented in an indoor environment, their retrieval accuracy and real-time positioning still have their own limitations. For this reason, this paper designs a visual indoor positioning system based on densely connected convolutional network image retrieval. Combine visual positioning with DenseNet-based image retrieval method. The problem of excessively deep network layers caused by the original convolutional neural network in pursuit of high retrieval accuracy is improved. Under the advantage of ensuring the high accuracy of image retrieval based on depth features, the problem of low real-time positioning caused by the long training time of the convolutional network model is improved. The simulation results show the feasibility of the positioning method in indoor environment, and the comparison experiment verifies the improvement of accuracy and speed as well as the reliability of the method.
