Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICRA.2015.7140094 | IEEE International Conference on Robotics and Automation |
Field | DocType | Volume |
Pairwise comparison,Point set registration,Bounded set,Matching (graph theory),Feature extraction,Frame rate,Geometry,Grid,Mathematics,Scalability | Conference | 2015 |
Issue | ISSN | Citations |
1 | 1050-4729 | 4 |
PageRank | References | Authors |
0.41 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuda Li | 1 | 7 | 0.80 |
| Andrew Calway | 2 | 645 | 54.66 |