Abstract
Precise localization is an essential issue for autonomous driving systems. 2D LiDAR has been widely used in various indoor localization systems because of its accuracy in measuring distances. However, the data of 2D LiDAR in the outdoor environment is very sparse and affected by dynamic objects greatly, which makes it very difficult to find the corresponding points. To overcome this defect, we propose a novel outdoor localization framework based on the fusion of stereo vision and 2D LiDAR, called FVL-OutLoc. This framework first adopts Dempster-Shafer theory to fuse several consecutive frames of 2D LiDAR data into a grid submap by using stereo odometry. Finally, Fourier-Mellin Transform is chosen to match the submap and prior map for further correcting the odometry. The experimental results on the KITTI dataset show that FVL-OutLoc can achieve high localization accuracy and run at a real-time performance.
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