Road Lane Semantic Segmentation for High Definition Map

High Definition map (HD Map) is an important part of autonomous driving vehicle. Most conventional method to generate HD map requires expensive system and post-processing of observed data. In this paper, we propose automatic HD map generating algorithm using just monocular camera without further human labors. The proposed algorithm detects road lane from image and classifies the type of road lane at pixel-level with Fully Convolutional Network (FCN) which outperforms the other semantic segmentation methods. The segmentation results are used to extract lane features, and the features are used for loop-closure detection. Final map is generated with graph-based Simultaneous Localization and Mapping (SLAM) algorithm. The experiment is done with monocular camera mounted on mobile vehicle. In this paper, final map generated by proposed method is compared with aerial view data. The results show that the proposed method can generate reliable map that is comparable to real roads even only the low-cost sensor is used.