In object perception for automated driving, Oriented Bounding Box(OBB) detection is important for extracting the position and direction of objects. However, previous studies on OBB detection dealt only with general situation such as L-shaped objects, and have lower performance for other different shaped objects. Thus, we focused on various shape such as U-shaped, Complex-shaped, and other non L-shaped objects that appear on road. In this paper, we propose a new OBB detection method robust to several challenging shapes and efficient to implement under realtime constraints. The proposed method encodes 3D points of each object into 2D grids, leading to the discretization effect that helps the rectangle fit the object. The oriented angle is extracted through our angle optimization approach using minimum loss solution based on the distance between the grid and the rectangle. Experiments were conducted with real-world data obtained with LiDAR and Differential-GPS(DGPS), and the results demonstrate the robustness and efficiency of the proposed method.