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

Pose Normalization of Indoor Mapping Datasets Partially Compliant to the Manhattan World Assumption