Abstract: With the popularity of smart phones and the Internet, the demand for high-precision positioning technology is also more significant, and the accurate positioning services have penetrated into various fields, such as the Internet of things, driverless, machine couriers, emergency rescue and so on. In outdoor environment, most of these services are provided by the global satellite navigation system. However, in indoor environment, such as deep mountain jungle, mine tunnel, basement and so on, GPS can not work properly due to signal attenuation and the influence of multi-path effect. Aiming at some special indoor scenes, this paper studies the visual inertial fusion navigation method based on loose coupling filter, and designs an indoor pedestrian positioning system for smartphone platform. In this method, the fast and robust sparse direct method is used in the visual front end, and the extended Kalman filter is used in the back end to fuse the inertia information. It can effectively integrate visual and inertial information, restore monocular visual scale, improve robustness, and achieve high precision indoor pedestrian location.

Key words: loose coupling filter, pedestrian navigation, extended Kalman filter, visual odometer, smartphone