  Light Detection and Ranging (LiDAR) based Simultaneous Localization and
Mapping (SLAM) has drawn increasing interests in autonomous driving. However,
LiDAR-SLAM suffers from accumulating errors which can be significantly
mitigated by Global Navigation Satellite System (GNSS). Precise Point
Positioning (PPP), an accurate GNSS operation mode independent of base
stations, gains more popularity in unmanned systems. Considering the features
of 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-SLAM
and PPP. For better integration, we derive LiDAR-SLAM positioning covariance by
using Singular Value Decomposition (SVD) Jacobian model, since SVD provides an
explicit analytic solution of Iterative Closest Point (ICP), which is a key
issue in LiDAR-SLAM. A novel method is then proposed to evaluate the estimated
LiDAR-SLAM covariance. In addition, to increase the reliability of GNSS in
urban canyon environment, we develop a LiDAR-SLAM assisted GNSS Receiver
Autonomous Integrity Monitoring (RAIM) algorithm. Finally, we validate
P$^3$-LOAM with UrbanNav, a challenging public dataset in urban canyon
environment. Comprehensive test results prove that P3-LOAM outperforms
benchmarks such as Single Point Positioning (SPP), PPP, LeGO-LOAM, SPP-LOAM,
and loosely coupled navigation system proposed by the publisher of UrbanNav in
terms of accuracy and availability.
