FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS

Abstract. With the growing interest in autonomous driving, accurate vehicle positioning remains an open problem, especially in urban environments. According to regulatory organisations, the vehicle positioning accuracy is required to be centimetre-level. As the most used positioning technique which provides globally referenced positioning solutions, GNSS is the fundamental component for realising real-time vehicle positioning, usually through the RTK approach. However, RTK requires a nearby reference station to enable integer ambiguity resolution for the ultra-precise carrier phase observations. In comparison, PPP makes use of State-Space Representation (SSR) corrections produced by global networks for satellite orbits and clocks to facilitate phase-based positioning. Moreover, IGS now offers Real-Time Service (RTS) to transmit such corrections. Notably, the major drawback of PPP is that it takes a long time to converge to precise solutions due to the carrier phase ambiguities being real-valued, which can be severely elongated when real-time corrections and low-cost GNSS receivers are used. In this paper, a tightly coupled positioning method is proposed, which shortens RT-PPP convergence to seconds by using lidar measurements referenced from an HD map through deep learning. The lidar measurements are generated by point cloud registration and weighted by their intensity values and geometric distributions, and are then combined with RT-PPP in an Extended Kalman-Filter (EKF), thus achieving fast convergence. Experimental results show that the proposed method achieves and maintains centimetre-level accuracy within 2 seconds using a low-cost UBLOX F9P receiver, which is a significant improvement as compared to the decimetre-level accuracy obtained from standalone RT-PPP.