Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering

The accuracy of satellite positioning results depends on the number of available satellites in the sky. In complex environments such as urban canyons, the effectiveness of satellite positioning is often compromised. To enhance the positioning accuracy of low-cost sensors, this paper combines the visual odometer data output by Xtion with the integrated navigation data output by the satellite receiver and MEMS IMU both in the mobile phone through adaptive Kalman filtering to improve positioning accuracy. Studies conducted in different experimental scenarios have found that in unobstructed environments, the RMSE accuracy of GNSS/IMU/visual fusion positioning improves by 50.4% compared to satellite positioning and by 24.4% compared to GNSS/IMU positioning. In obstructed environments, the RMSE accuracy of GNSS/IMU/visual fusion positioning improves by 57.8% compared to satellite positioning and by 36.8% compared to GNSS/IMU integrated positioning.