Abstract
For safe autonomous driving, localization is essential. Conventional point cloud matching methods using convergence calculations have the problem of initial position dependency. While methods that predict the vehicle's position as a likelihood distribution reduce initial position dependency, but they have difficulty handling the 3D information of the point cloud. This study proposes a matching method that retains the features of LiDAR point clouds in a pillar structure and converts them into pseudo-images, using deep learning to estimate the vehicle's position as a likelihood distribution. The proposed method aims to achieve more robust localization compared to conventional point cloud matching methods.
