A Novel LiDAR-Camera Fusion Method for Enhanced Odometry

The development of Autonomous Vehicles (AVs) provides users with high-quality services and convenient travel experiences. As one of the most important functions in the automotive field, mobile positioning has attracted widespread attention from scholars. However, using a single-modal sensor (LiDAR or camera) poses challenges for precise localization due to their measurement flaws. Therefore, some scholars have proposed Visual-LiDAR Odometry (VLO). Nevertheless, most of the existing VLO solely use a single-modal sensor as their main framework and utilize another sensor for optimization, which does not fully leverage the complementary behavior of sensors in different environments. Thus, this paper presents a novel LiDAR-camera fusion method for improving the odometry estimation. Firstly, we employ a depth completion network to convert the image into pseudo-LiDAR to compensate for the missing depth values in the LiDAR point clouds. Then, we adopt Bayesian inference to enhance the robustness of the fusion method in different environments. Finally, evaluations on the public KITTI odometry show that the proposed method outperforms several state-of-the-art methods.