Integration of Multi-GNSS PPP-RTK/INS/Vision with a Cascading Kalman Filter for Vehicle Navigation in Urban Areas

Abstract

Precise point positioning (PPP) has received much attention in recent years for its low cost, high accuracy, and global coverage. Nowadays, PPP with ambiguity resolution and atmospheric augmentation is widely regarded as PPP-RTK (real-time kinematic), which weakens the influence of the long convergence time in PPP and regional service coverage in RTK. However, PPP-RTK cannot work well in urban areas due to limitations of non-line-of-sight (NLOS) conditions. Inertial navigation systems (INS) and vision can realize continuous navigation but suffer from error accumulation. Accordingly, the integration model of multi-GNSS (global navigation satellite system) and PPP-RTK/INS/vision with a cascading Kalman filter and dynamic object removal model was proposed to improve the performance of vehicle navigation in urban areas. Two vehicular tests denoted T01 and T02 were conducted in urban areas to evaluate the navigation performance of the proposed model. T01 was conducted in a relatively open-sky environment and T02 was collected in a GNSS-challenged environment with many obstacles blocking the GNSS signals. The positioning results show that the dynamic object removal model can work well in T02. The results indicate that multi-GNSS PPP-RTK/INS/vision with a cascading Kalman filter can achieve a positioning accuracy of 0.08 m and 0.09 m for T01 in the horizontal and vertical directions and 0.83 m and 0.91 m for T02 in the horizontal and vertical directions, respectively. The accuracy of the velocity and attitude estimations is greatly improved by the introduction of vision.

Keywords

• PPP
• multi-GNSS
• PPP-RTK/INS/vision integration
• cascading Kalman filter
• urban vehicle navigation
• dynamic object removal