A unified cycle-slip, multipath estimation, detection and mitigation method for VIO-aided PPP in urban environments

Accurate, continuous and reliable positioning is required in autonomous driving. The precise point positioning (PPP) technique, which can provide a global accurate positioning service using a single global navigation satellite system (GNSS) receiver, has attracted much attention. Nevertheless, due to the cycle slips and multipath effects in the GNSS signal, the performance of PPP is severely degraded in urban areas, which has a negative effect on the PPP/inertial navigation system (INS)/vision integrated navigation. Moreover, the carrier phase observations with un-modeled multipath cause false detection of small cycle slips and lead to deviation in the state variable estimation in PPP. Therefore, an effective cycle slip/multipath estimation, detection and mitigation (EDM) method is proposed. A clustering method is used to separate the cycle slips and multipath from the carrier phase observations aided by visual inertial odometry (VIO) positioning results. The influence of the carrier phase multipath on state variable estimation is reduced by adjusting the stochastic ambiguity model in the Kalman filter. The proposed EDM method is validated by vehicle experiments conducted in urban and freeway areas. Experimental results demonstrate that 0.2% cycle slip detection error is achieved by our method. Besides, the multipath estimation accuracy of EDM improves by more than 50% compared with the geometry-based (GB) method. Regarding positioning accuracy, the EDM method has a maximum of 72.2% and 63.2% improvement compared to traditional geometry-free (GF) and GB methods.