GNSS-Assisted LiDAR Odometry and Mapping for Urban Environment

Abstract

Simultaneous localization and mapping (SLAM) technology has been widely used in space exploration, unmanned driving, and service robots. In practice, light detection and ranging (LiDAR) odometry and mapping in real-time (LOAM) algorithm has delivered excellent results. However, LOAM and its variants still face challenges in terms of accuracy and robustness. This article presents a novel LiDAR-global navigation satellite system (GNSS) SLAM framework that aims to obtain high-precision and real-time localization and mapping. To address this, this article extends the LOAM pipeline by integrating a dual-antenna GNSS into the original framework. Specifically, we first propose a vector angle-based feature point extraction method. GNSS is then loosely coupled into the LiDAR odometry module to improve the environmental robustness. Furthermore, a method for adaptively calculating the GNSS precision factor based on the LiDAR point cloud is proposed, which can effectively adjust the weight of GNSS in the fusion framework. Finally, a novel GNSS-LiDAR fusion framework with a curve deformation-based fusion method is presented to achieve accuracy and robust localization and mapping performance. The proposed method is extensively evaluated on public and real-world datasets. In all tests, the proposed SLAM system shows reliable and robust localization and mapping performance in comparison with the state-of-the-art SLAM methods.