PEDESTRIAN INERTIAL NAVIGATION ALGORITHM BASED ON SCENE RECOGNITION

Abstract. As indoor location-based services become increasingly essential to people's daily life, it is necessary to build a stable and accurate indoor pedestrian positioning system. The foot-mounted inertial navigation system can provide a short-term robust solution but suffer from error accumulation over time. To alleviate this issue, this paper proposes a pedestrian inertial navigation algorithm based on scene recognition to reduce the heading drift. Based on the hypothesis that corridors in buildings are generally narrow and straight and pedestrians have a high probability to walk in straight lines in corridors, we use a scene recognition model to assist foot-mounted INS. When the scene recognition model determines that the pedestrian is walking in a corridor, a straight-line constraint will be implemented to reduce the heading drift and improve the observability of the vertical gyroscope bias. Experiments show that the algorithm can effectively improve the navigation performance and the observability of vertical gyroscope bias when the sensor biases are significant.