3D Map Generation with Shape and Appearance Information

Taro Yamada, Shuichi Enokida

2022

Abstract

It is clear from the numerous reports of recent years that interest in the research and development of autonomous mobile robots is growing and that a key requirement for the successful development of such self- directed machines is effective estimations of the navigable domain. Furthermore, in view of the differing characteristics of their physical performance capabilities relative to specific applications, specific estimations must be made for each robot. The effective assessment of a domain that permits successful robot navigation of a densely occupied indoor space requires the generation of a fine-grained three-dimensional (3D) map to facilitate its safe movements. This, in turn, requires the provision of appearance information as well as space shape ascertainment. To addresFs these issues, we herein propose a practical Semantic Simultaneous Localization and Mapping (Semantic SLAM) method capable of yielding labeled 3D maps. This method generates maps by class-labeling images obtained via semantic segmentation of 3D point groups obtained with Real-Time Appearance-Based Mapping (RTAB-Map).

Paper Citation

in Harvard Style

Yamada T. and Enokida S. (2022). 3D Map Generation with Shape and Appearance Information. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 743-748. DOI: 10.5220/0010812900003124

in Bibtex Style

@conference{visapp22,
author={Taro Yamada and Shuichi Enokida},
title={3D Map Generation with Shape and Appearance Information},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={743-748},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010812900003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - 3D Map Generation with Shape and Appearance Information
SN - 978-989-758-555-5
AU - Yamada T.
AU - Enokida S.
PY - 2022
SP - 743
EP - 748
DO - 10.5220/0010812900003124
PB - SciTePress