SGHD: Semantic-Geometric Combined 3-D Histogram Descriptor for Global Localization in Autonomous Vehicles

LiDAR-based global localization plays a crucial role in autonomous driving and robotic systems. However, real-time changes in environmental conditions present challenges for global localization. Current methods predominantly rely on sparse LiDAR point clouds with single-feature information (semantic or geometric), leading to insufficient accuracy and robustness in 6-DOF pose estimation and similarity matching. In this work, we propose a novel semantic-geometric combined 3D histogram descriptor, called the SGHD descriptor, with robust place recognition and 6-DoF pose estimation capabilities. We first extract semantic and geometric features of objects through operations such as point cloud clustering and semantic segmentation. Next, we describe the distribution of neighboring objects using topological structures. To improve object discriminability, we construct triangular descriptors for each object based on semantic information and relative distance. Subsequently, we consider the complementary nature of semantic and geometric information and propose a 3D histogram descriptor. This 3D histogram descriptor encodes semantic, spatial angle and relative distance information, enhancing the invariance of the triangle descriptor. We perform similarity discrimination and 6-DOF pose estimation through coarse-to-fine matching of local and global semantic maps. Overall, the proposed descriptor achieves high-accuracy and high-robustness 6-DOF pose estimation and place recognition for global localization. We extensively compared the proposed SGHD descriptor with state-of-the-art methods on the public SemanticKITTI dataset. Quantitative results demonstrate that SGHD exhibits higher adaptability and significant improvements compared to its counterparts. The implementation of SGHD will be released at https://github.com/wf-hahaha/SGHD