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Simultaneous pose and reliability estimation using convolutional neural network and Rao–Blackwellized particle filter
http://hdl.handle.net/2237/00029245
http://hdl.handle.net/2237/000292454baf925a-c031-46e8-a052-8647b3346c77
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
paper (1.2 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-02-06 | |||||
| タイトル | ||||||
| タイトル | Simultaneous pose and reliability estimation using convolutional neural network and Rao–Blackwellized particle filter | |||||
| 言語 | en | |||||
| 著者 |
Akai, Naoki
× Akai, Naoki× Morales, Luis Yoichi× Murase, Hiroshi |
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| アクセス権 | ||||||
| アクセス権 | open access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
| 権利 | ||||||
| 言語 | en | |||||
| 権利情報 | This is an Accepted Manuscript of an article published by Taylor & Francis Group in Advanced Robotics on 27/08/2018, available online: http://www.tandfonline.com/10.1080/01691864.2018. | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Localization | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | failure detection | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | reliability | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | convolutional neural network | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Rao–Blackwellized particle filter | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this study, we propose a novel localization approach that simultaneously estimates the reliability of estimation results. In the approach, a convolutional neural network (CNN) is used to make decision whether the localization process has failed or not. We train the CNN using a dataset that includes successful localization results and faults. However, the decision will contain some noise and many misdetection results may occur when the decision made by the CNN is used directly to detect faults. Therefore, we estimate both a robot's pose and reliability of the localization results based on the decision. To simultaneously estimate the robot's pose and reliability, we propose a new graphical model and implement a Rao–Blackwellized particle filter based on the model. We evaluated the proposed approach based on simulations and actual environments, which showed that the reliability estimated by the proposed approach can be used as an exact criterion for detecting localization faults. In addition, we show that the proposed approach can be applied in actual environments even when a dataset created from a simulation is used to train the CNN. | |||||
| 言語 | en | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | ファイル公開：2019/08/27 | |||||
| 言語 | ja | |||||
| 出版者 | ||||||
| 出版者 | Taylor & Francis | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| 出版タイプ | ||||||
| 出版タイプ | AM | |||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1080/01691864.2018.1509726 | |||||
| ISSN（print） | ||||||
| 収録物識別子タイプ | PISSN | |||||
| 収録物識別子 | 0169-1864 | |||||
| ISSN（Online） | ||||||
| 収録物識別子タイプ | EISSN | |||||
| 収録物識別子 | 1568-5535 | |||||
| 書誌情報 |
en : Advanced Robotics 巻 32, 号 17, p. 930-944, 発行日 2018-08-27 |
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| 著者版フラグ | ||||||
| 値 | author | |||||
