Precise Vehicle Position and Heading Estimation Using a Binary Road Marking Map. (20th January 2019)

• Record Type: Journal Article
• Authors: Kim, Kyu-Won; Im, Jun-Hyuck; Heo, Moon-Beom; Jee, Gyu-In
• Other Names: Marletta Vincenzo Academic Editor.
• Is Part Of: Journal of sensors. Volume 2019(2019)
• Publication Date: 2019-01-20
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2019/1296175
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Road markings are always present on roads to guide and control traffic. Therefore, they can be used at any time for vehicle localization. Moreover, they can be easily extracted by using light detection and ranging (LIDAR) intensity because they are brightly colored. We propose a vehicle localization method using a 2D road marking grid map. The grid map inserts the map information into the grid directly. Thus, an additional process (such as line detection) is not required and there is no problem due to false detection. We obtained road marking using a 3D LIDAR (Velodyne HDL-32E) and binarized this information to store in the map. Thus, we could reduce the map size significantly. In the previous research, the road marking grid map was used only for position estimation. However, we propose a position-and-heading estimation algorithm using the binary road marking grid map. Accordingly, we derive more precise position estimation results. Moreover, position reliability is an important factor for vehicle localization. Autonomous vehicles may cause accidents if they cannot maintain their lane momentarily. Therefore, we propose an algorithm for evaluating map matching results. Consequently, we can use only reliable matching results and increase position reliability. The experiment was conducted in Gangnam, Seoul, where GPS error occurs largely. In the experimental results, the lateral root mean square (RMS) error was 0.05 m and longitudinal RMS error was 0.08 m. Further, we obtained a position error of less than 50 cm in both lateral and longitudinal directions with a 99% confidence level.