| Title | Exploiting Segmentation for Robust 3D Object Matching |
| Publication Type | Conference Paper |
| Year of Publication | 2012 |
| Authors | Krainin M, Konolige K, Fox D |
| Conference Name | ICRA |
| Abstract | <p>While Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point clouds, they do not take into account constraints arising from sensor viewpoints. More recent beam-based models take into account sensor noise and viewpoint, but problems still remain. In particular, good optimization strategies are still lacking for the beam-based model. In situations of occlusion and clutter, both beam-based and ICP approaches can fail to find good solutions. In this paper, we present both an optimization method for beam-based models and a novel framework for modeling observation dependencies in beam-based models using over-segmentations. This technique enables reasoning about object extents and works well in heavy clutter. We also make available a ground-truth 3D dataset for testing algorithms in this area.</p> |
| Downloads | http://www.cs.washington.edu/ai/Mobile_Robotics/postscripts/sbm-icra-12.pdf PDF |
| Citation Key | Kra12Exp |
