  Visual odometry is the process of estimating the position and orientation of
a camera by analyzing the images associated to it. This paper develops a quick
and accurate approach to visual odometry of a moving RGB-D camera navigating on
a static environment. The proposed algorithm uses SURF (Speeded Up Robust
Features) as feature extractor, RANSAC (Random Sample Consensus) to filter the
results and Minimum Mean Square to estimate the rigid transformation of six
parameters between successive video frames. Data from a Kinect camera were used
in the tests. The results show that this approach is feasible and promising,
surpassing in performance the algorithms ICP (Interactive Closest Point) and
SfM (Structure from Motion) in tests using a publicly available dataset.
