Incremental structural modeling on sparse visual SLAM

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

Research output: Chapter or section in a book/report/conference proceedingChapter 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 languageEnglish
Title of host publicationProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PublisherIEEE
Pages30-33
Number of pages4
ISBN (Electronic)9784901122160
DOIs
Publication statusPublished - 19 Jul 2017
Event15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
Duration: 8 May 201712 May 2017

Publication series

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

Conference

Conference15th IAPR International Conference on Machine Vision Applications, MVA 2017
Country/TerritoryJapan
CityNagoya
Period8/05/1712/05/17

Bibliographical note

Publisher Copyright:
© 2017 MVA Organization All Rights Reserved.

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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