Visual Odometry for RGB-D Cameras
This work addresses camera localization for robotics or AR applications, but it is incremental as it combines existing techniques.
The paper tackled visual odometry for RGB-D cameras by developing a method using SURF, RANSAC, and Minimum Mean Square to estimate camera motion, showing it outperformed ICP and SfM algorithms in tests on a public dataset.
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.