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

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Abstract

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.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to thank the Centre National d’Etudes Spatiales (CNES) for real time orbit and clock products. We also thank the Institute of Geodesy and Geophysics (IGG) of the Chinese Academy of Sciences (CAS) in Wuhan for their Differential Code Bias (DCB) products. This work is sponsored by The National Key Research and Development Program of China (2021YFB2501100).

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Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China

    Bo Xu, Shoujian Zhang & Xingxing Li

  2. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China

    Kaifa Kuang

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  1. Bo Xu
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  2. Shoujian Zhang
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  3. Kaifa Kuang
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  4. Xingxing Li
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Contributions

BX and SZ conceived and designed the algorithm; BX performed the experiments, analyzed the data and drafted the paper; XL contributed the experiment datasets; KK and SZ revised the manuscript. All authors read and approved the final manuscript

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Correspondence to Shoujian Zhang.

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Xu, B., Zhang, S., Kuang, K. et al. A unified cycle-slip, multipath estimation, detection and mitigation method for VIO-aided PPP in urban environments. GPS Solut 27, 59 (2023). https://doi.org/10.1007/s10291-023-01396-7

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