2020年7月
Robust and Efficient Indoor Localization Using Sparse Semantic Information from a Spherical Camera
Sensors
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- 巻
- 20
- 号
- 15
- 開始ページ
- 4128
- 終了ページ
- 4128
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.3390/s20154128
- 出版者・発行元
- {MDPI} {AG}
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.
- リンク情報
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- DOI
- https://doi.org/10.3390/s20154128
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/32722263
- URL
- https://www.mdpi.com/1424-8220/20/15/4128
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85088560000&origin=inward 本文へのリンクあり
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85088560000&origin=inward
- ID情報
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- DOI : 10.3390/s20154128
- ISSN : 1424-8220
- ORCIDのPut Code : 78019566
- PubMed ID : 32722263
- SCOPUS ID : 85088560000