A Robust Localization Approach Based on Point Cloud Descriptor and SLAM in Urban Environment

Abstract

Abstract The importance of autonomous driving localization to autonomous driving systems cannot be overstated, reliable and high-precision localization is the focus of research in autonomous driving. Many studies at this stage use multiple sensors to generate high-definition maps for matching and localization. The labor and economic costs required to maintain such maps are often high. This paper proposes a fusion localization framework for simultaneous localization and mapping (SLAM) and matching localization. It could afford high-precision localization results of autonomous vehicles using only light detection and ranging (LiDAR) sensors. Concretely, a novel global point cloud descriptor, named binary scan context (BSC) is proposed, for matching localization. It encodes the structural features in the z-height direction in the point cloud space in a binary way. An efficient two-stage matching strategy is proposed to improve the efficiency of matching. Experiments on public datasets and vehicles have demonstrated the validation and precision of the method.