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

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.