Live structural modeling using RGB-D SLAM

Nicolas Olivier; Hideaki Uchiyama; Masashi Mishima; Diego Thomas; Rin Ichiro Taniguchi; Rafael Roberto; Joao Paulo Lima; Veronica Teichrieb

doi:10.1109/ICRA.2018.8460973

Live structural modeling using RGB-D SLAM

Nicolas Olivier, Hideaki Uchiyama, Masashi Mishima, Diego Thomas, Rin Ichiro Taniguchi, Rafael Roberto, Joao Paulo Lima, Veronica Teichrieb

Department of Mechanical Engineering

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

5 Citations (SciVal)

Abstract

This paper presents a method for localizing primitive shapes in a dense point cloud computed by the RGB-D SLAM system. To stably generate a shape map containing only primitive shapes, the primitive shape is incrementally modeled by fusing the shapes estimated at previous frames in the SLAM, so that an accurate shape can be finally generated. Specifically, the history of the fusing process is used to avoid the influence of error accumulation in the SLAM. The point cloud of the shape is then updated by fusing the points in all the previous frames into a single point cloud. In the experimental results, we show that metric primitive modeling in texture-less and unprepared environments can be achieved online.

Original language	English
Title of host publication	2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Publisher	IEEE
Pages	6352-6358
Number of pages	7
ISBN (Electronic)	9781538630815
DOIs	https://doi.org/10.1109/ICRA.2018.8460973
Publication status	Published - 10 Sept 2018
Event	2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia Duration: 21 May 2018 → 25 May 2018

Publication series

Name	Proceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)	1050-4729

Conference

Conference	2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Country/Territory	Australia
City	Brisbane
Period	21/05/18 → 25/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

Software
Control and Systems Engineering
Artificial Intelligence
Electrical and Electronic Engineering

Access to Document

10.1109/ICRA.2018.8460973

Cite this

Olivier, N., Uchiyama, H., Mishima, M., Thomas, D., Taniguchi, R. I., Roberto, R., Lima, J. P., & Teichrieb, V. (2018). Live structural modeling using RGB-D SLAM. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 (pp. 6352-6358). Article 8460973 (Proceedings - IEEE International Conference on Robotics and Automation). IEEE. https://doi.org/10.1109/ICRA.2018.8460973

Live structural modeling using RGB-D SLAM. / Olivier, Nicolas; Uchiyama, Hideaki; Mishima, Masashi et al.
2018 IEEE International Conference on Robotics and Automation, ICRA 2018. IEEE, 2018. p. 6352-6358 8460973 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Olivier, N, Uchiyama, H, Mishima, M, Thomas, D, Taniguchi, RI, Roberto, R, Lima, JP & Teichrieb, V 2018, Live structural modeling using RGB-D SLAM. in 2018 IEEE International Conference on Robotics and Automation, ICRA 2018., 8460973, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, pp. 6352-6358, 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, 21/05/18. https://doi.org/10.1109/ICRA.2018.8460973

@inproceedings{675575b41a34411ca19b18e55552c2cb,

title = "Live structural modeling using RGB-D SLAM",

abstract = "This paper presents a method for localizing primitive shapes in a dense point cloud computed by the RGB-D SLAM system. To stably generate a shape map containing only primitive shapes, the primitive shape is incrementally modeled by fusing the shapes estimated at previous frames in the SLAM, so that an accurate shape can be finally generated. Specifically, the history of the fusing process is used to avoid the influence of error accumulation in the SLAM. The point cloud of the shape is then updated by fusing the points in all the previous frames into a single point cloud. In the experimental results, we show that metric primitive modeling in texture-less and unprepared environments can be achieved online.",

author = "Nicolas Olivier and Hideaki Uchiyama and Masashi Mishima and Diego Thomas and Taniguchi, {Rin Ichiro} and Rafael Roberto and Lima, {Joao Paulo} and Veronica Teichrieb",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 ; Conference date: 21-05-2018 Through 25-05-2018",

year = "2018",

month = sep,

day = "10",

doi = "10.1109/ICRA.2018.8460973",

language = "English",

series = "Proceedings - IEEE International Conference on Robotics and Automation",

publisher = "IEEE",

pages = "6352--6358",

booktitle = "2018 IEEE International Conference on Robotics and Automation, ICRA 2018",

address = "USA United States",

}

TY - GEN

T1 - Live structural modeling using RGB-D SLAM

AU - Olivier, Nicolas

AU - Uchiyama, Hideaki

AU - Mishima, Masashi

AU - Thomas, Diego

AU - Taniguchi, Rin Ichiro

AU - Roberto, Rafael

AU - Lima, Joao Paulo

AU - Teichrieb, Veronica

PY - 2018/9/10

Y1 - 2018/9/10

N2 - This paper presents a method for localizing primitive shapes in a dense point cloud computed by the RGB-D SLAM system. To stably generate a shape map containing only primitive shapes, the primitive shape is incrementally modeled by fusing the shapes estimated at previous frames in the SLAM, so that an accurate shape can be finally generated. Specifically, the history of the fusing process is used to avoid the influence of error accumulation in the SLAM. The point cloud of the shape is then updated by fusing the points in all the previous frames into a single point cloud. In the experimental results, we show that metric primitive modeling in texture-less and unprepared environments can be achieved online.

AB - This paper presents a method for localizing primitive shapes in a dense point cloud computed by the RGB-D SLAM system. To stably generate a shape map containing only primitive shapes, the primitive shape is incrementally modeled by fusing the shapes estimated at previous frames in the SLAM, so that an accurate shape can be finally generated. Specifically, the history of the fusing process is used to avoid the influence of error accumulation in the SLAM. The point cloud of the shape is then updated by fusing the points in all the previous frames into a single point cloud. In the experimental results, we show that metric primitive modeling in texture-less and unprepared environments can be achieved online.

UR - http://www.scopus.com/inward/record.url?scp=85061697007&partnerID=8YFLogxK

U2 - 10.1109/ICRA.2018.8460973

DO - 10.1109/ICRA.2018.8460973

M3 - Chapter in a published conference proceeding

AN - SCOPUS:85061697007

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 6352

EP - 6358

BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018

PB - IEEE

T2 - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018

Y2 - 21 May 2018 through 25 May 2018

ER -

Live structural modeling using RGB-D SLAM

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this