Centimeter-accurate vehicle navigation in urban environments with a tightly integrated PPP-RTK/MEMS/vision system

Emerging applications like autonomous cars and unmanned aerial vehicles demand accurate, continuous and reliable positioning. The PPP-RTK technique, which could provide a rapid centimeter-accurate positioning service using a single GNSS receiver, has been recognized as a promising tool for mass-market and automotive applications. Nevertheless, the positioning performance of PPP-RTK degrades in urban environments because of the frequent signal deteriorating and blocking. Inertial navigation system (INS) is commonly integrated with GNSS to improve the continuity, accuracy and reliability of precise positioning as it has several advantages of all-environment operability and high temporal resolution, but it is limited by rapid error accumulation in long-term operation, especially when a microelectromechanical system inertial measurement unit (MEMS-IMU) is employed. Fortunately, the camera, another low-cost sensor, which provides rich information about the surrounding environment, is expected to improve the navigation performance further. This contribution develops a tightly integrated PPP-RTK/MEMS/vision model to achieve continuous and accurate positioning in urban environments. The raw data of MEMS-IMU and a stereo camera, as well as the high-precision GNSS phase measurements, are fused based on a multistate constraint Kalman filter to fully exploit the complementary properties from heterogeneous sensors. On this basis, a fast ambiguity resolution is achievable with the augmentation of the high-precision INS/vision information and the precise atmospheric corrections. The proposed integrated system is validated by several vehicle experiments conducted in urban areas. Results indicate that a centimeter-level accuracy of 4.1, 2.2 and 7.3 cm in the east, north and up components, respectively, and a high fixing percentage of 96.8% can be achieved for PPP-RTK/MEMS/vision in an urban environment, which exhibits comparable performance with respect to the tight integration of PPP-RTK and a tactical IMU. Besides, ambiguity refixing can be implemented instantaneously for the integrated system when going under three consecutive overpasses in 25 s.

Automated summary

This paper introduces a tightly integrated PPP-RTK/MEMS/vision model for continuous and accurate positioning in urban environments. By fusing the raw data of MEMS-IMU and a stereo camera, as well as high-precision GNSS phase measurements, using a multistate constraint Kalman filter, the complementary properties of heterogeneous sensors are fully exploited. Vehicle experiments in urban areas validate that the integrated system can achieve high-precision positioning.