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Light Detection and Ranging (LiDAR) based Simultaneous Localization andMapping (SLAM) has drawn increasing interests in autonomous driving. However,LiDAR-SLAM suffers from accumulating errors which can be significantlymitigated by Global Navigation Satellite System (GNSS). Precise PointPositioning (PPP), an accurate GNSS operation mode independent of basestations, gains more popularity in unmanned systems. Considering the featuresof the two technologies, LiDAR-SLAM and PPP, this paper proposes a SLAM system,namely P3-LOAM (PPP based LiDAR Odometry and Mapping) which couples LiDAR-SLAMand PPP. For better integration, we derive LiDAR-SLAM positioning covariance byusing Singular Value Decomposition (SVD) Jacobian model, since SVD provides anexplicit analytic solution of Iterative Closest Point (ICP), which is a keyissue in LiDAR-SLAM. A novel method is then proposed to evaluate the estimatedLiDAR-SLAM covariance. In addition, to increase the reliability of GNSS inurban canyon environment, we develop a LiDAR-SLAM assisted GNSS ReceiverAutonomous Integrity Monitoring (RAIM) algorithm. Finally, we validateP$^3$-LOAM with UrbanNav, a challenging public dataset in urban canyonenvironment. Comprehensive test results prove that P3-LOAM outperformsbenchmarks such as Single Point Positioning (SPP), PPP, LeGO-LOAM, SPP-LOAM,and loosely coupled navigation system proposed by the publisher of UrbanNav interms of accuracy and availability.

P3-LOAM: PPP/LiDAR Loosely Coupled SLAM with Accurate Covariance Estimation and Robust RAIM in Urban Canyon Environment