PERFORMANCE ANALYSIS OF SEMANTIC REFRESH INDOOR NAVIGATION FOR SMARTPHONE'S SENSORS USING INS/VINS INTEGRATION SCHEME

Positioning and Orientation System (POS), which integrates Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), is widely used to accomplish outdoor navigation missions for land vehicles. However, the positioning accuracy would become worse in GNSS-hostile environments (Chiang et al., 2013), which is quite challenging to accomplish indoor navigation environments. Nevertheless, smartphones are contained many embedded sensors, including GNSS, IMU, camera, which have the potential to be an ideal personal navigation device. In this research, we mainly propose an integrated scheme of INS/VINS/object detection refresh (ODR) for indoor challenging environments. The goal is to achieve indoor navigation for vehicular applications only using smartphones. The algorithm is developed based on the smartphone. By the conventional inertial navigation system, which is integrated with two designed processes to further improve the performance. First is assistance from the visual-inertial navigation system (VINS). The long-term drift caused by the INS could be decreased effectively, and complete the extended Kalman filter (EKF) composition. The second is to apply neural network, YOLO-v3 (Redmon et al.,2018), to detect objects and provide the object's describer information to refresh the proper position. Therefore, the proposed method uses visual estimation and recognition methods to assist the smartphone platform to obtain a more accurate solution. Finally, we use the navigation-grade IMU as the reference system for accuracy verification. The accuracy comparisons of the three integration solutions are analysed reasonably. The position accuracy is reasonable. Compared with the original smartphone INS integration method, the proposed integration scheme improves the accuracy from the horizontal direction by 78.5%.