Visual odometry is a key technology for the lo-calization of autonomous robots in real-world environments. In particular, when a robot adopts a camera as an external sensor, visual odometry can provide useful information for fine-grained localization. Many approaches have been developed for this task; however, this study proposes a novel scheme that uses traditional feature extraction and tracking to realize computationally efficient but sufficiently accurate for practical use when used with a visual navigation scheme based on semantic segmentation. The key features of the proposed scheme can be summarized as follows: the elimination of feature points on moving obstacles, feature point extraction on object boundaries, feature tracking, and outlier elimination, considering semantic information. The proposed scheme reduced the estimation error of visual odometry to 12% of the existing scheme in the best case, according to experimental results, using datasets created with the CARLA simulator.