7170

Exploiting Segmentation for Robust 3D Object Matching

Michael Krainin, Kurt Konolige, Dieter Fox
RSE: Robotics and State Estimation Lab, Department of Computer Science and Engineering, University of Washington
ICRA, 2012

@article{krainin2012exploiting,

   title={Exploiting Segmentation for Robust 3D Object Matching},

   author={Krainin, M. and Konolige, K. and Fox, D.},

   year={2012}

}

Download Download (PDF)   View View   Source Source   Source codes Source codes

Package:

768

views

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 beambased 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 groundtruth 3D dataset for testing algorithms in this area.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: