Research on Deep Learning-Based Indoor Visual Localization Technology

At present, indoor positioning technology generally has some problems such as the need for pre-deployment of equipment, poor anti-interference ability and high cost. To solve these problems, this paper designs a lightweight network model based on deep learning technology, and uses pure visual method to deduce indoor positioning coordinates according to indoor scenes. Firstly, since there is no open data set for visual positioning of Indoor scenes at present, an Indoor Dataset 600(ID600) dataset was built by ourselves. Then, different lightweight network models were trained on SF-XL(Small) outdoor geographic image positioning data set to investigate the positioning accuracy and reasoning speed of different models. Finally, the structure adjustment and training strategy of the model are optimized on ID600. After optimization, the number of parameters in the Local-SqueezeNet model is only 1.14M, and the Top-1 accuracy reaches 91.4% when the Distance threshold is set to 1200mm.