Incremental structural modeling on sparse visual SLAM

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 引用 (Scopus)

抜粋

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.

元の言語英語
ホスト出版物のタイトルProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
出版者Institute of Electrical and Electronics Engineers Inc.
ページ30-33
ページ数4
ISBN(電子版)9784901122160
DOI
出版物ステータス出版済み - 7 19 2017
イベント15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, 日本
継続期間: 5 8 20175 12 2017

出版物シリーズ

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

その他

その他15th IAPR International Conference on Machine Vision Applications, MVA 2017
日本
Nagoya
期間5/8/175/12/17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

これを引用

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