Research on Combination Positioning Based on Natural Features and Gyroscopes for AR on Mobile Phones

Considering the situation that performing indoor positioning system on mobile platform mostly relying on additional wireless signal and known environmental information, we propose an autonomous combination positioning method aimed at continuously estimating metric position and pose in an unknown scene. The vision-based SLAM algorithm extracts the image characteristics of the environment for producing a 3D map of point features and tracking erratic hand-held motion robustly. The position and pose calculated by SLAM algorithm are viewed as observation of an extended Kalman filter to correct the pose obtained from SINS. The initial value of metric scale of position is calculated using Least Squares which ensures the fast convergence of EKF. SINS is also used to detect the failures of vision part and improve robustness of tracking results in the environment with rare characteristics. The experimental results demonstrate that our system can produce detailed maps with thousands of landmarks which can be tracked at about 30HZ with high accuracy and robustness, although the computational resource is limited on current smart phones.