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

He, Guojian and Yuan, Xingda and Zhuang, Yan and Hu, Huosheng (2020) 'An Integrated GNSS/LiDAR-SLAM Pose Estimation Framework for Large-Scale Map Building in Partially GNSS-Denied Environments.' IEEE Transactions on Instrumentation and Measurement, 70. ISSN 0018-9456

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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.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 06 Jul 2021 11:16
Last Modified: 06 Jul 2021 11:16
URI: http://repository.essex.ac.uk/id/eprint/30707

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