# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 1 Introduction
We believe that the combination of visual signal and PDR will be one of the solutions for indoor positioning. Therefore, it is meaningful to combine computer vision with PDR, which may better solve the problem of PDR position correction and work in a three-dimensional environment. Visual position recognition is one of the visual methods that may be used to correct PDR. VPR is the task of identifying a place through a visual image of it. VPR is suitable to fill the gaps of positioning errors because of its high robustness and convenience of requiring no additional deployment. The principle of VPR is matching feature descriptors of the query image and other labeled images to find the most similar one. Then, from the label of the most similar image, we can get the location of the query image. Traditional visual place recognition algorithms use local or global feature method, such as SIFT [10], SURF [11], CenSurE [12], Gist [13] and ADMM-RS [14] to get the descriptors of images. However, each local and global descriptor loses some information of the image [15], which may result in a wrong match. Beside, the calculation of these traditional methods will cost a lot of computing resources and are too slow to use, which do not suit the real-time location. With the development of deep learning technology, CNNs (Convolutional Neural Networks) are used to extract feature descriptors from images and have made some improvements in recognition tasks [16; 17; 18]. CNNs can obtain the deep information in the images, so that they can focus on the deeper differences in the images, and thus can achieve better visual positioning effect. In VPR, CNN performs better than the traditional method and costs less time. In our method, CNN is used to build a real-time visual position recognition module. In this paper, a novel and improved indoor localization method is proposed, which can effectively reduce accumulative errors derived from PDR and works well in a three-dimensional space by fusing vision information. Especially, we train a CNN architecture for indoor visual place recognition tasks and use it to aid PDR to gain better performance in indoor positioning. We use the VPRsystem to get a starting point for PDR, while the place coordinates are obtained from VPR to modify the localization results from PDR. The improved fusion indoor localization method has much less computing complexity than VPR system and makes PDR more flexible and accurate. The main contributions of this article are: (1) A new method called V-PDR is proposed for indoor localization which combines the VPR and PDR. With the system, we can obtain the absolute position rather than the relative position. (2) V-PDR improves the localization accuracy and solves the problem that PDR cannot work properly in three-dimensional space by combining vision and inertial sensors. The rest parts of this paper is arranged as follows. Section 2 is the related works for pedestrian dead reckoning. In Section 3, our PDR system is illustrated. Our design for the fusion indoor localization method is introduced in Section 4, which include our VPR network design and fusion method. Details of experiments implemented in our lab for testing our method and the results are listed in Section 5. Finally, the conclusions are drawn in the last section.