Hierarchical Topometric Representation of 3D Robotic Maps

TL;DR

This paper introduces a hierarchical topometric mapping method for 3D robotic environments, capable of handling complex structures and multi-storey point clouds, producing versatile maps for various robotics tasks.

Contribution

The novel method creates a hierarchical volumetric topological map from 3D point clouds, accommodating complex building structures and multi-storey data, enhancing robotic mapping capabilities.

Findings

Effective handling of multi-storey point clouds.

Generation of multi-dimensional metric maps.

Validated on multiple datasets with positive results.

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 $passage$s (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.