  Recent decades have witnessed a significant increase in the use of visual
odometry(VO) in the computer vision area. It has also been used in varieties of
robotic applications, for example on the Mars Exploration Rovers. This paper,
firstly, discusses two popular existing visual odometry approaches, namely
LSD-SLAM and ORB-SLAM2 to improve the performance metrics of visual SLAM
systems using Umeyama Method. We carefully evaluate the methods referred to
above on three different well-known KITTI datasets, EuRoC MAV dataset, and TUM
RGB-D dataset to obtain the best results and graphically compare the results to
evaluation metrics from different visual odometry approaches. Secondly, we
propose an approach running in real-time with a stereo camera, which combines
an existing feature-based (indirect) method and an existing feature-less
(direct) method matching with accurate semidense direct image alignment and
reconstructing an accurate 3D environment directly on pixels that have image
gradient. Keywords VO, performance metrics, Umeyama Method, feature-based
method, feature-less method & semi-dense real-time.
