  In this paper, we present a novel pose normalization method for indoor
mapping point clouds and triangle meshes that is robust against large fractions
of the indoor mapping geometries deviating from an ideal Manhattan World
structure. In the case of building structures that contain multiple Manhattan
World systems, the dominant Manhattan World structure supported by the largest
fraction of geometries is determined and used for alignment. In a first step, a
vertical alignment orienting a chosen axis to be orthogonal to horizontal floor
and ceiling surfaces is conducted. Subsequently, a rotation around the
resulting vertical axis is determined that aligns the dataset horizontally with
the coordinate axes. The proposed method is evaluated quantitatively against
several publicly available indoor mapping datasets. Our implementation of the
proposed procedure along with code for reproducing the evaluation will be made
available to the public upon acceptance for publication.
