Multi-Correspondence 3-D Pose Estimation
Shuda Li and Andrew Calway
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, espe- cially 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.
Absolute pose estimation using multiple forms of correspondences from RGB-D frames, Shuda Li and Andrew Calway, Proceedings of the IEEE International conference on Robotics and Automation (ICRA), Stockholm, 2016. DOI.