An Integrated GNSS/LiDAR-SLAM Pose Estimation Framework for Large-Scale Map Building in Partially GNSS-Denied Environments

Abstract

This article presents an integrated global navigation satellite system/light detection and ranging (GNSS/LiDAR)-based simultaneous localization and mapping (SLAM) pose estimation framework to perform large-scale 3-D map building in partially GNSS-denied outdoor environments. The framework takes the advantage of the complementarity between GNSS positioning and LiDAR-SLAM to decompose the map building task according to the GNSS real-time kinematic (RTK) status. When mapping in GNSS-denied scenes, a 3-D LiDAR-SLAM algorithm is adopted to estimate poses and a correction algorithm is presented to correct drift errors. On the other hand, when mapping in open scenes, a GNSS-initialized LiDAR mapping algorithm (GL-mapping) is proposed to loosely couple GNSS positioning and LiDAR data registration. It can perform the orientation estimation without the use of either the high-cost inertial sensing device or the GNSS dual-antenna. Experiments are conducted in large-scale outdoor environments to demonstrate that the proposed framework can accomplish simultaneous pose estimation and map building with high precision in both open scenes and GNSS-denied scenes.

Mobile robot 3D trajectory estimation on a multilevel surface with multimodal fusion of 2D camera features and a 3D light detection and ranging point cloud

“…The information from visual reference is combined with IMU. He et al 40 integrate global navigation satellite based on simultaneous localization and mapping pose estimation performing large-scale 3D map building. The authors used global positioning for pose estimation.…”

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

“…Similarly, 3D LiDAR was also fused with RTK and IMU using EKF in [37]. In the works above, IMU is crucial in converting global and local coordinates, but it is actually not a necessary component, as exemplified in [38], [39], [40] and [41]. Joerger et al [38] used Kalman Filtering to fuse 2D LiDAR and GPS, resulting in a robust localization in forest scenario and urban canyon scenario.…”

“…Shamsudin et al [42] extended Rao-Blackwellized particle filtering to fuse RTK and 3D LiDAR. He et al [39] used an optimization-based algorithm to integrate RTK and 3D LiDAR in partially GNSS-denied environment. In [40], the authors used a RAIM framework to integrate LiDAR odometry and GNSS by UKF.…”

Mitigation of GNSS Time Synchronization Attacks in a Multicorrelator Receiver

“…The external measurement-assistance algorithms were implemented by detecting abnormal positions and timing results. The assistance includes the use of the inertial navigation system (INS) [22], LiDAR [23], and vision sensor [24]. However, the complex algorithms and high costs of the above mentioned methods limit their application.…”