Recently, a vehicle is equipped with various sensors, which aim smart and autonomous functions. Single-view odometer estimates its pose using a monoscopic camera mounted on a vehicle. It was generally studied in the field of computer vision. On the other hands, photogrammetry focuses to produce precise three-dimensional position information using bundle adjustment methods. Therefore, this paper proposes to apply photogrammetric approach to single view odometer. Firstly, it performs real-time corresponding point extraction. Next, it estimates the pose using relative orientation based on coplanarity conditions. Then, scale calibration is performed to convert the estimated translation in the model space to the translation in the real space. Finally, absolute orientation is performed using more than three images. In this step, we also extract the appropriate model points through verification procedure. For experiments, we used the data provided by KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) community. This technique took 0.12 seconds of processing time per frame. The rotation estimation error was about 0.005 degree per meter and the translation estimation error was about 6.8 %. The results of this study have shown the applicability of photogrammetry to visual odometry technology.