Multipath Inflation Factor for Robust GNSS/IMU/VO Fusion-Based Navigation in Urban Areas

Global navigation satellite systems (GNSS), integrated with an inertial measurement unit (IMU) and visual sensors, are widely used for vehicular navigation. With the advancement of emerging vehicular technologies, the performance requirements for positioning, navigation, and timing (PNT) have become critical, emphasizing not only positioning accuracy but also high reliability. However, GNSS signals are susceptible to reflection and diffraction in urban environments, leading to multipath effects, such as non-line-of-sight (NLOS) reception and multipath interference. The GNSS positioning errors will increase significantly, causing the integrated navigation system to fail to meet the high-performance navigation requirements. To address this issue, we have proposed a robust GNSS/IMU/visual odometry (VO) fusion algorithm with a new GNSS weighting model and an adaptive VO velocity measurement update algorithm for urban navigation. In particular, a multipath inflation factor, based on real-time IMU and VO data, is proposed for the GNSS weighting model to mitigate multipath effects. A VO variance attenuation factor based on zero velocity detection in a robust extended Kalman filter (REKF) is also designed to adaptively adjust the covariance of VO measurements, enhancing the overall robustness of the integrated system. A field test was conducted in urban environments. The results show that the proposed algorithm achieves horizontal and 3-D positioning accuracy of 3.38 m and 5.00 m, respectively, outperforming the conventional GNSS/IMU/VO integration using C/N0-based weighting model. The improvements in horizontal and 3-D positioning accuracy are 63.4% and 56.1%, respectively.