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

Abstract

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 growing popularity in unmanned systems. Considering the features of the two technologies, LiDAR-SLAM and PPP, this paper proposes a SLAM system, namely P 3 -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, in terms of accuracy and availability, P 3 -LOAM outperforms benchmarks such as Single Point Positioning (SPP), PPP, LeGO-LOAM, SPP-LOAM, and the loosely coupled navigation system proposed by the publisher of UrbanNav.

M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots

“…d) GNSS: GNSS is a valuable localization source that can achieve high-precision positioning outdoors. Coupling GNSS raw measurements into SLAM systems has been proven effective in advancing the localization performance in urban canyons, as shown in recent work [24]. Besides, a sky-pointing camera can help monitor satellite availability and further improve localization accuracy [25].…”

Hierarchical fusion based high precision SLAM for solid-state lidar

“…Zhang et al [21] employed segmentation and clustering techniques to process the features and improve accuracy through extraction. Li et al [22] presented a scheme that integrates LiDAR SLAM with Precise Point Positioning, using the Jacobi model to derive the LiDAR covariance and enable autonomous monitoring of Global Navigation Satellite System receivers. Despite the computational efficiency of the loosely coupled approach, it is important to note that information loss can occur when the LiDAR is decoupled from the inertial constraints [23].…”

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

“…The motion estimation of 3D points is dealt with using perspective-n-point (PnP) [46] or iterative close point (ICP) [47]. Several approaches have been proposed to solve PnP and ICP [48], including direct linear transformation (DLT) [49] and singular value decomposition (SVD) [50].…”