This work is focused on global registration of surface models such as homogeneous triangle meshes and point clouds. The investigated approach utilizes feature descriptors in order to assign correspondences between the data sets and to reduce complexity by considering only characteristic feature points. It is based on the decomposability of rigid motions into a rotation and a translation. The space of rotations is searched with a particle filter and scoring is performed by looking for clusters in the resulting sets of translations. We use features computed from homogeneous triangle meshes and point clouds that require low computation time. A major advantage of the approach proves to be the possible consideration of prior knowledge about the relative orientation. This is especially important when high noise levels produce deteriorated features that are hard to match correctly. Comparisons to existing algorithms show the method’s competitiveness, and results in robotic applications with different sensor types are presented.
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