Exploiting Segmentation for Robust 3D Object Matching
Proc. of International Conference on Robotics and Automation (ICRA), 2012
Abstract
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.