RGBD Relocalisation Using Pairwise Geometry and Concise Key Point Sets (Source codes released!!!)

by Shuda Li and Andrew Calway.
University of Bristol, UK
publised in IEEE Intl. Conf. on Robotics and Automation (ICRA), 2015

• [github]
• [paper]
• [demo 1] [demo 2]
• [talk]

Abstract

We describe a novel RGBD relocalisation algorithm based on key point matching. It combines two components. First, a graph matching algorithm which takes into account the pairwise 3-D geometry amongst the key points, giving robust relocalisation. Second, a point selection process which provides an even distribution of the `most matchable’ points across the scene based on non-maximum suppression within voxels of a volumetric grid. This ensures a bounded set of matchable key points which enables tractable and scalable graph matching at frame rate. We present evaluations using a public dataset and our own more difficult dataset containing large pose changes, fast motion and non-stationary objects. It is shown that the method significantly out performs state-of-the-art methods.