Pose Normalization of Indoor Mapping Datasets Partially Compliant to the Manhattan World Assumption
This work addresses pose normalization for indoor mapping data, which is incremental as it improves robustness for non-ideal Manhattan World structures.
The paper tackles the problem of aligning indoor mapping datasets that only partially follow Manhattan World geometry by identifying the dominant structure for pose normalization. The method achieves robust alignment on publicly available datasets, with code provided for reproducibility.
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.