# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 1 Introduction  
Location information and location-based services are always hot topics. At present, GNSS (Global Navigation Satellite System)-based positioning methods are still dominant for outdoor environments. However, in complex indoor environments, the satellite signals received by users is so weak that the GNSS-based positioning method will fail, which makes people turn to other effective techniques to obtain accurate the information of indoor location. Commonly, indoor positioning methods include RFID (Radio Frequency Identification), Bluetooth, IR (Infrared Ray), geomagnetism, WLAN (Wireless Local Area Network) and visual recognition [1]. In principle, indoor localization methods can be divided into five categories: triangulation, sensing, recognition, fingerprint and DR (dead reckoning) [2]. In these methods, RFID, Bluetooth, IR and WLAN require deploying additional equipment, resulting in an increased cost. These methods require special equipment to be laid and have special signal receiving equipment, which has a higher requirement on the scene. Meanwhile, magnetic-based positioning method is vulnerable to interference, which affects the positioning accuracy. Compared with other methods, DR does not rely on external environments and additional equipment, which can gain high accuracy in a short time. It achieves low-cost and independent localization through processing outputs of inertial sensors. With the development of MEMS (Micro Electro Mechanical System) technology, we pay more attention to DR. However, limited by cumulative error of gyroscopes, the positioning error will gradually increase when workingcontinuously for long periods. Furthermore, DR can only work in a two-dimensional plane without additional information, which means that DR has trouble in dealing with pedestrians going up- and downstairs. To reduce the cumulative error and extend application scenarios, we need to find an algorithm that can help PDR correct its cumulative error to increase its accuracy. There are many studies focused on the problem of cumulative error in PDR. A smartPDR using mobile phones has been proposed, which uses accelerometers, magnetometers and gyroscopes in mobile phones, proposing an indoor positioning model that can detect downstairs, headings, etc. for localization [3]. Another algorithm combines GNSS and PDR for positioning and navigation and uses GNSS for positioning correction [4]. In [5], a combination of GPS and dead reckoning is used to explore the trajectory of animal movement to help understand ecology. [PERSON] [6] combined WI-FI and PDR and used the Gaussian process to estimate and construct the RSS fingerprints by K-nearest weight. In [7], RFID, PDR and magnetic matching are combined for positioning, where RSS technology and a floor map are used. In addition, some research focuses on PDR working in the three-dimensional environment. The air pressure is combined with the PDR system to solve the upstairs and downstairs problems. The air pressure of the sensor on the hand will change regularly with the swing of the hand. PDR can be realized by processing the air pressure signal [8]. [PERSON] et al. [9] fused the magnetic signals and the PDR by Kalman filter and the PF scheme. The locations are obtained by the weighted mean of particles. It reduces the effect of heavy magnetic distortions. These works have achieved great results in the indoor location. These researches provide us with a wide range of ways for improving PDR. Following their research, we aim to combine a more determined method with PDR, which might work better. Visual information is easy to obtain with low cost sensors and is easily identified by humans. The visual information includes common RGB pictures, including depth RGBD pictures, binocular camera pictures and other pictures. RGB images might be the simplest and lowest cost visual information. In addition, computer vision now delivers amazing results in a variety of areas, including image recognition and image classification, which are beneficial for indoor location.