# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 5 Experiments and Results
### VPR Test Result  
Our network training process was performed on an GTX1080 Ti GPU. The margin parameter \(\alpha\) was set to 0.2. We trained the model for 40 epochs, the learning rate was 0.0002 for the first 15 epochs, and was reduced by 90% every 15 epochs. The training was carried out on the Indoor Scene Recognition dataset [39], which contains 67 indoor categories and a total of 15,620 images. All images are in jpg format and there are at least 100 images per category in the dataset. It took about 8 h for training. After the network was trained, it was necessary to test the model to see if the trained model is accurate enough to adapt to the V-PDR algorithm. We made two tests, respectively, on a subset of the training dataset and a dataset of our laboratory. In the experiment, we set the doorway as the origin, and east and north as the x-axis and y-axis. Note that, in actual use, since the location does not need to know the actual height but only the actual floor information, the 3D location information can be directly used as the label. In addition, we set the floor as the z-axis. Then, every location was  
Figure 8: Fusion flow chart of V-PDR system. set to a point with its location coding (x, y, z). Every 1 m we set a point. In every point, we obtained 18 images every 20 degrees. Note that, each image of one degree in different point was in the same direction. Overall, we obtained a dataset consisting of 36 locations and 648 images. In this way, the vector dataset was composed of the vectors and their labels, which consisted of location and direction. Sample images of the Indoor Scene Recognition and our laboratory datasets are shown in Figures 9 and 10, respectively. Finally, we performed VPR model test experiments on indoor datasets and photos collected in our laboratory. First, images were extracted into the vector. After that, we compared the vector similarity in the vector dataset to check whether the predictions are correct. We used the Recall@1 to show the accuracy. As for indoor datasets, we used the test dataset for testing. For our laboratory dataset, a subset of the images we obtained was used for testing. Figure 10: Sample images of our Laboratory. Figure 9: Sample images in Indoor Scene Recognition dataset. The VPR model obtains a result of a Recall@1 (@1 denotes one of top database candidates) of 80% on the Indoor Scene Recognition dataset and a recall@1 of near 92% on our laboratory. Note that the scenes in the Indoor Scene Recognition dataset are more complex than those in our laboratory. Some scenes are very similar, thus it performs worse than it in our laboratory. Performing VPR positioning once takes about 1.3 s. Since the VPR is only called occasionally, the speed is acceptable. Analyzing the results in our laboratory dataset, we can find that good positioning results can be achieved for most images. However, for a few locations with incorrect positioning, it can be found that most of their positioning errors are due to the low similarity of all the images, even as low as 20%, resulting in inaccurate positioning. Such positioning errors can be repaired in subsequent V-PDR without affecting the overall positioning. Through testing, we found that our VPR model can identify the location well. This VPR is effective enough for our V-PDR algorithm. It is used as an intermittent auxiliary positioning and can be well adapted to actual use. In addition, we performed an experiment to show the error between VPR route and the ground truth to show the VPR problems. We walked around our laboratory. For VPR positioning, in the experiment, the VPR worked for about 3 m walking, and the location is where the image is most similar. The positioning line is regarded as a walking path. The results are shown in Figure 11.  
In Figure 11, the ground truth is shown in red while the VPR route is in black. The red triangle is where we used the VPR to locate while the black triangle is the location predicted by the VPR.