P3-LOAM: PPP/LiDAR Loosely Coupled SLAM with Accurate Covariance Estimation and Robust RAIM in Urban Canyon Environment
This research addresses the problem of improving localization accuracy and reliability for autonomous driving systems operating in challenging urban canyon environments, which is a significant problem for the autonomous vehicle industry.
This paper proposes P3-LOAM, a loosely coupled SLAM system integrating LiDAR-SLAM and Precise Point Positioning (PPP) to mitigate error accumulation in LiDAR-SLAM and improve GNSS reliability. The system achieves superior accuracy and availability compared to several benchmarks, including SPP, PPP, LeGO-LOAM, SPP-LOAM, and a loosely coupled navigation system, when tested on the UrbanNav dataset.
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.