Robust tracking and monocular 3D reconstruction of line-based features in urban environments

Driving assistance and safety systems are based on data processing of different sensor information. The environment of a vehicle is detected by these sensing elements for accident avoidance or reduction of the accident severity. Due to package size and cost reasons, only selective sensors are used for standard-production of a vehicle. These integrated sensors have to fulfill several tasks at once and to serve different applications. Especially the processed data of a grayscale camera is used for parking assistant or lane detection system. But known from computer vision, a three-dimensional reconstruction of feature elements is possible by analyzing image sequences. This research considers corner detection to create point clouds in space. But the dominant elements of an urban environment, more precisely buildings, are characterized by edges. With this paper we focus on the robust tracking and three-dimensional reconstruction of line-based features. In this context we present our algorithm, defining and analyzing edge tolerances, which is called Tube Principle. The accuracy of this approach is determined by comparing the three-dimensional lines with landmarks of high-precise maps.