This paper presents a new algorithm for the self-localization of a mobile robot using one degree perspective invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of a simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two locally parallel side-lines are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points. Feature points for cross ratio are extracted robustly using a vanishing point and intersection points between two locally parallel side-lines and vertical lines. Also the local position estimation problem has been treated when feature points exist less than 4 points in the viewed scene. The robustness and feasibility of our algorithms have been demonstrated through real world expreiments in indoor environments using an indoor mobile robot, KASIRI-II (KAist SImple Roving Intelligence).