Incremental structural modeling on sparse visual SLAM

Rafael Roberto; Hideaki Uchiyama; Joao Paulo Lima; Hajime Nagahara; Rin Ichiro Taniguchi; Veronica Teichrieb

doi:10.23919/MVA.2017.7986765

Incremental structural modeling on sparse visual SLAM

Rafael Roberto, Hideaki Uchiyama, Joao Paulo Lima, Hajime Nagahara, Rin Ichiro Taniguchi, Veronica Teichrieb

Department of Mechanical Engineering

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

2 Citations (SciVal)

Abstract

This paper presents an incremental structural modeling approach that improves the precision and stability of existing batch based methods for sparse and noisy point clouds from visual SLAM. The main idea is to use the generating process of point clouds on SLAM effectively. First, a batch based method is applied to point clouds that are incrementally generated from SLAM. Then, the temporal history of reconstructed geometric primitives is statistically merged to suppress incorrect reconstruction. The evaluation shows that both precision and stability are improved compared to a batch based method and the proposed method is suitable for real-time structural modeling.

Original language	English
Title of host publication	Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
Publisher	IEEE
Pages	30-33
Number of pages	4
ISBN (Electronic)	9784901122160
DOIs	https://doi.org/10.23919/MVA.2017.7986765
Publication status	Published - 19 Jul 2017
Event	15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan Duration: 8 May 2017 → 12 May 2017

Publication series

Name	Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

Conference

Conference	15th IAPR International Conference on Machine Vision Applications, MVA 2017
Country/Territory	Japan
City	Nagoya
Period	8/05/17 → 12/05/17

Bibliographical note

Publisher Copyright:
© 2017 MVA Organization All Rights Reserved.

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.23919/MVA.2017.7986765

Cite this

Roberto, R., Uchiyama, H., Lima, J. P., Nagahara, H., Taniguchi, R. I., & Teichrieb, V. (2017). Incremental structural modeling on sparse visual SLAM. In Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 (pp. 30-33). Article 7986765 (Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017). IEEE. https://doi.org/10.23919/MVA.2017.7986765

Incremental structural modeling on sparse visual SLAM. / Roberto, Rafael; Uchiyama, Hideaki; Lima, Joao Paulo et al.
Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017. IEEE, 2017. p. 30-33 7986765 (Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017).

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

Roberto, R, Uchiyama, H, Lima, JP, Nagahara, H, Taniguchi, RI & Teichrieb, V 2017, Incremental structural modeling on sparse visual SLAM. in Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017., 7986765, Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017, IEEE, pp. 30-33, 15th IAPR International Conference on Machine Vision Applications, MVA 2017, Nagoya, Japan, 8/05/17. https://doi.org/10.23919/MVA.2017.7986765

Roberto R, Uchiyama H, Lima JP, Nagahara H, Taniguchi RI, Teichrieb V. Incremental structural modeling on sparse visual SLAM. In Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017. IEEE. 2017. p. 30-33. 7986765. (Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017). doi: 10.23919/MVA.2017.7986765

@inproceedings{bbaaf5c26203477fab3315f787de4371,

title = "Incremental structural modeling on sparse visual SLAM",

abstract = "This paper presents an incremental structural modeling approach that improves the precision and stability of existing batch based methods for sparse and noisy point clouds from visual SLAM. The main idea is to use the generating process of point clouds on SLAM effectively. First, a batch based method is applied to point clouds that are incrementally generated from SLAM. Then, the temporal history of reconstructed geometric primitives is statistically merged to suppress incorrect reconstruction. The evaluation shows that both precision and stability are improved compared to a batch based method and the proposed method is suitable for real-time structural modeling.",

author = "Rafael Roberto and Hideaki Uchiyama and Lima, {Joao Paulo} and Hajime Nagahara and Taniguchi, {Rin Ichiro} and Veronica Teichrieb",

note = "Publisher Copyright: {\textcopyright} 2017 MVA Organization All Rights Reserved.; 15th IAPR International Conference on Machine Vision Applications, MVA 2017 ; Conference date: 08-05-2017 Through 12-05-2017",

year = "2017",

month = jul,

day = "19",

doi = "10.23919/MVA.2017.7986765",

language = "English",

series = "Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017",

publisher = "IEEE",

pages = "30--33",

booktitle = "Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017",

address = "USA United States",

}

TY - GEN

T1 - Incremental structural modeling on sparse visual SLAM

AU - Roberto, Rafael

AU - Uchiyama, Hideaki

AU - Lima, Joao Paulo

AU - Nagahara, Hajime

AU - Taniguchi, Rin Ichiro

AU - Teichrieb, Veronica

PY - 2017/7/19

Y1 - 2017/7/19

N2 - This paper presents an incremental structural modeling approach that improves the precision and stability of existing batch based methods for sparse and noisy point clouds from visual SLAM. The main idea is to use the generating process of point clouds on SLAM effectively. First, a batch based method is applied to point clouds that are incrementally generated from SLAM. Then, the temporal history of reconstructed geometric primitives is statistically merged to suppress incorrect reconstruction. The evaluation shows that both precision and stability are improved compared to a batch based method and the proposed method is suitable for real-time structural modeling.

AB - This paper presents an incremental structural modeling approach that improves the precision and stability of existing batch based methods for sparse and noisy point clouds from visual SLAM. The main idea is to use the generating process of point clouds on SLAM effectively. First, a batch based method is applied to point clouds that are incrementally generated from SLAM. Then, the temporal history of reconstructed geometric primitives is statistically merged to suppress incorrect reconstruction. The evaluation shows that both precision and stability are improved compared to a batch based method and the proposed method is suitable for real-time structural modeling.

UR - http://www.scopus.com/inward/record.url?scp=85027847952&partnerID=8YFLogxK

U2 - 10.23919/MVA.2017.7986765

DO - 10.23919/MVA.2017.7986765

M3 - Chapter in a published conference proceeding

AN - SCOPUS:85027847952

T3 - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

SP - 30

EP - 33

BT - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

PB - IEEE

T2 - 15th IAPR International Conference on Machine Vision Applications, MVA 2017

Y2 - 8 May 2017 through 12 May 2017

ER -

Incremental structural modeling on sparse visual SLAM

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this