Vehicle Localization Based on the Detection of Line Segments from Multi-Camera Images

For realizing autonomous vehicle driving and advanced safety systems, it is necessary to achieve accurate vehicle localization in cities. This paper proposes a method of accurately estimating vehicle position by matching a map and line segment features detected from images captured by a camera. Features such as white road lines, yellow road lines, road signs, and curb stones, which could be used as clues for vehicle localization, were expressed as line segment features on a two-dimensional road plane in an integrated manner. The detected line segments were subjected to bird's-eye view transformation to transform them to the vehicle coordinate system so that they could be used for vehicle localization regardless of the camera configuration. Moreover, an extended Kalman filter was applied after a detailed study of the line observation errors for realizing real-time estimation. Vehicle localization was tested under city driving conditions, and the vehicle position was identified with sub-meter accuracy.