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

TL;DR

This paper introduces a robust pose normalization technique for indoor mapping datasets that can handle deviations from the Manhattan World assumption, improving alignment accuracy in complex indoor environments.

Contribution

A novel pose normalization method that effectively aligns indoor mapping data even when geometries partially deviate from the Manhattan World assumption.

Findings

Method successfully aligns datasets with multiple Manhattan systems.

Quantitative evaluation shows improved robustness over existing approaches.

Implementation and evaluation code will be publicly available.

Abstract

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.