Tracking incremental movements and accurately analyzing the environment for localization is one of the challenges of autonomous robots. The approach is to overcome these challenges to estimating the translational and rotational movements using a paired camera by analyzing the captured images, known as Visual Odometry. In this paper, we build a simple feature-based stereo visual odometry system. This system consists of six main parts: detecting features and computing their descriptors using Oriented FAST and Rotated BRIEF (ORB), matching features brute force based on Hamming distance from feature descriptors, tracking features using Lucas-Kanade optical flow, triangulating points features using linear triangulation, estimating translation and rotation by solving Perspective-n-Point (PnP) problems using a combination of Efficient PnP (EPnP) and Random Sample Consensus (RANSAC) methods, and updating position and orientation estimates. Our system has an average translation root mean squared error of 5.1284% and an average rotation error of 0.027 deg/m on the KITTI public odometry dataset with a performance speed of 18.88 frames per second on a 1-core computer environment with a clock speed of 2.7 GHz.