Robust and Real-Time Outdoor Localization Only With a Single 2-D LiDAR

Abstract

Robust and real-time localization is an essential issue for autonomous driving. As a high-precision sensor, light detection and ranging (LiDAR) is widely used in autonomous driving. However, because LiDAR is vulnerable to environmental factors, such as fog, rain, and dynamic objects, robust localization based on LiDAR remains challenging. Moreover, the 3-D LiDAR is prohibitively expensive for consumer-grade applications. A robust and real-time outdoor localization method based solely on a single 2-D LiDAR is proposed to overcome these deficiencies. First, a grid matching algorithm based on data association is proposed to remove mismatches caused by great noises. Then, the Dempster–Shafer evidence theory-based strategy is proposed to fuse several frames of consecutive 2-D LiDAR data in a local window into a local map to eliminate dynamic objects. Finally, the generated local map is matched with the preconstructed global map to fulfill localization. Extensive experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) dataset validate the proposed method. It achieves high accuracy and real-time performance comparable and even superior to the 3-D LiDAR-based localization method.