Current smartphones have become to be embedded with global navigation satellite system (GNSS), IMU, camera and other sensors which are capable of providing user position, velocity and attitude. However, it is hard to utilize their actual positioning and navigation performance capabilities due to the disparate sensors, software technologies adopted among manufacturers, and the high influence of environmental conditions. Therefore, a unified processing solution is necessary. In this research, we proposed a scheme that integrates the measurements of IMU, GNSS chipset and camera of smartphone through the extended Kalman filter (EKF) to enhance the navigation performance. The visual measurement is pre-processed by ORB-SLAM and re-scaled by the assistance of GNSS measurement before utilized to update the integration filter. In order to verify the performance of integrated system, the field test data was collected in the downtown area of Tainan city, Taiwan. Experimental results indicate that vision contributes significantly in improving the accuracy of position as well as attitude, especially for the yaw angle. It verified that the proposed integrated system using data from smartphone’s sensors is helpful and efficient in the aspect of increasing the accuracy in GNSS-challenging environments.

Keywords: Integration, Smartphone, ORB-SLAM, GNSS-challenging environments