This thesis explains how Oriented FAST and rotated BRIEF SLAM (ORB-SLAM), one of the best visual SLAM solutions, works indoor and evaluates the technique performance for three different cameras: monocular camera, stereo camera and RGB-D camera. Three experiments are designed to find the limitation of the algorithm. From the experiments, the RGB-D SLAM gives the most accurate result for the indoor environment. The monocular SLAM performs better than stereo SLAM on our platform due to limited computation power. It is expected that stereo SLAM provides better results by increasing the experimental platform computational power. The ORBSLAM results demonstrate the applicability of the approach for the autonomous navigation and future autonomous cars.