by Shuda Li and Andrew Calway
University of Bristol, UK
accepted by IEEE Intl. Conf. on Robotics and Automation (ICRA), 2016
We describe a new approach to absolute pose estimation from noisy and outlier contaminated matching point sets for RGB-D sensors. We show that by integrating multiple forms of correspondence based on 2-D and 3-D points and surface normals gives more precise, accurate and robust pose estimates. This is because it gives more constraints than using one form alone and increases the available measurements, especially when dealing with sparse matching sets. We demonstrate the approach by incorporating it within a RANSAC algorithm and introduce a novel direct least-square approach to calculate pose estimates. Results from experiments on synthetic and real data demonstrate improved performance over existing methods.