In this paper, we propose the Road-SLAM algorithm, which robustly exploits road markings obtained from camera images. Road markings are well categorized and informative but susceptible to visual aliasing for global localization. To enable loop-closures using road marking matching, our method defines a feature consisting of road markings and surrounding lanes as a sub-map. The proposed method uses random forest method to improve the accuracy of matching using a sub-map containing road information. The random forest classifies road markings into six classes and only incorporates informative classes to avoid ambiguity. The proposed method is validated by comparing the SLAM result with RTK-Global Positioning System (GPS) data. Accurate loop detection improves global accuracy by compensating for cumulative errors in odometry sensors. This method achieved an average global accuracy of 1.098 m over 4.7 km of path length, while running at real-time performance.