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Erschienen in: Autonomous Robots 5/2021

12.06.2021

Hierarchical topometric representation of 3D robotic maps

verfasst von: Zhenpeng He, Hao Sun, Jiawei Hou, Yajun Ha, Sören Schwertfeger

Erschienen in: Autonomous Robots | Ausgabe 5/2021

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Abstract

In this paper, we propose a method for generating a hierarchical, volumetric topological map from 3D point clouds. There are three basic hierarchical levels in our map: \(storey - region - volume\). The advantages of our method are reflected in both input and output. In terms of input, we accept multi-storey point clouds and building structures with sloping roofs or ceilings. In terms of output, we can generate results with metric information of different dimensionality, that are suitable for different robotics applications. The algorithm generates the volumetric representation by generating volumes from a 3D voxel occupancy map. We then add passages (connections between volumes), combine small volumes into a big region and use a 2D segmentation method for better topological representation. We evaluate our method on several freely available datasets. The experiments highlight the advantages of our approach.

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Fußnoten
1
If this requirement is not met, the z-axis normal could be estimated by Principal Component Analysis (PCA). There are also other methods suitable for this task, such as the Manhattan frame estimation proposed by Ghanem et al. (2015) for more complicated cases.
 
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Metadaten
Titel
Hierarchical topometric representation of 3D robotic maps
verfasst von
Zhenpeng He
Hao Sun
Jiawei Hou
Yajun Ha
Sören Schwertfeger
Publikationsdatum
12.06.2021
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 5/2021
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-021-09991-8

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