LiDAR-Visual Fusion SLAM for Autonomous Vehicle Location

Abstract

The simultaneous localization and mapping (SLAM) is indispensable to Autonomous Vehicle (AV). However, the visual images are susceptible to light interference, and light detection and ranging (LiDAR) depends heavily on geometric features of the surrounding scene, relying solely on a camera or LiDAR exhibits limitations in challenging environments. To solve these problems, we propose an LiDAR-visual fusion method for high precision and robust vehicle localization. Compared with the previous LiDAR-visual fusion method, the proposed method fully utilizes the sensor’s measurement data for fusion in each part. First, an LiDAR vision frame is constructed at the front end, then the LiDAR is used to assist the vision in obtaining the depth information and tracking. In the closed-loop recognition part, a logic judgment module is introduced, and the LiDAR point cloud assists in the vision for loop closure correction to reduce the positioning error. Additionally, a visual-assisted LiDAR method for 3-D scene reconstruction is proposed. Experiments in real scenes show that the average positioning errors are 2.065, 1.9, and 2.9 cm in x, y, and z-directions, respectively; and the average rotation errors are 0.11 rad, 0.11 rad, and 0.13 rad in roll, pitch, yaw. The average positioning time is 29.98 ms. Compared with the classical ORB-SLAM2, LeGO-LOAM, DEMO, and TVL-SLAM algorithms, the proposed method demonstrates superior accuracy, robustness, and real-time performance.