Feature augmented PPP-RTK/INS/Vision integration based on combinatorial optimization for urban vehicle navigation

Abstract Multi-sensor fusion and precise point positioning real-time kinematic (PPP-RTK) techniques are gaining popularity in vehicle navigation studies. The integration of the Global Navigation Satellite System (GNSS), inertial navigation system (INS), and vision can significantly improve the accuracy and continuity of positioning. The methods used to integrate different measurements are usually divided into an extended Kalman filter and factor graph optimization (FGO). We propose an integration method of PPP-RTK/INS/Vision that utilizes both the square-root information filter (SRIF) and FGO, combining the merits of the two algorithms. The proposed PPP-RTK/INS/Vision integration consists of two parts: the first is reweighted PPP-RTK/INS tightly coupled integration based on SRIF, and the second is GNSS/INS/Vision integration based on a two-step FGO method with feature selection in the vision component. The first positioning result is integrated with the FGO-based visual INS with uniform depth distribution to generate the final navigation information. The real-world experiment, carried out in an urban area, includes many GNSS-challenged environments, such as high buildings, tunnels, and viaducts. The results demonstrate that the proposed method achieves a root mean square of 0.57, 0.65, and 0.86 m in the north, east, and down components of the entire trajectory, respectively. Compared to the PPP-RTK/INS/Vision tightly coupled integration with a multi-state constrained Kalman filter, it shows an 11.0% improvement in vertical accuracy and comparable horizontal accuracy. Moreover, in the GNSS-challenged environment, the performance of the proposed method is more outstanding in the vertical component, with an improvement of 83.0% in the vertical direction and similar horizontal accuracy. Our study provides a novel approach to integrating both measurements and optimal estimation methods.