{"_buckets": {"deposit": "2142d2e8-e446-4a7c-a52d-65357a60e816"}, "_deposit": {"id": "27045", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "27045"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00027045", "sets": []}, "author_link": ["88591", "88592", "88593"], "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2018-08-27", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "17", "bibliographicPageEnd": "944", "bibliographicPageStart": "930", "bibliographicVolumeNumber": "32", "bibliographic_titles": [{"bibliographic_title": "Advanced Robotics", "bibliographic_titleLang": "en"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "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\u0027s pose and reliability of the localization results based on the decision. To simultaneously estimate the robot\u0027s pose and reliability, we propose a new graphical model and implement a Rao\u2013Blackwellized 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.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_description_5": {"attribute_name": "\u5185\u5bb9\u8a18\u8ff0", "attribute_value_mlt": [{"subitem_description": "\u30d5\u30a1\u30a4\u30eb\u516c\u958b\uff1a2019/08/27", "subitem_description_language": "ja", "subitem_description_type": "Other"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "Taylor \u0026 Francis", "subitem_publisher_language": "en"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1080/01691864.2018.1509726", "subitem_relation_type_select": "DOI"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "This is an Accepted Manuscript of an article published by Taylor \u0026 Francis Group in Advanced Robotics on 27/08/2018, available online: http://www.tandfonline.com/10.1080/01691864.2018.", "subitem_rights_language": "en"}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_10_source_id_61": {"attribute_name": "ISSN\uff08print\uff09", "attribute_value_mlt": [{"subitem_source_identifier": "0169-1864", "subitem_source_identifier_type": "PISSN"}]}, "item_10_source_id_62": {"attribute_name": "ISSN\uff08Online\uff09", "attribute_value_mlt": [{"subitem_source_identifier": "1568-5535", "subitem_source_identifier_type": "EISSN"}]}, "item_1615787544753": {"attribute_name": "\u51fa\u7248\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_ab4af688f83e57aa", "subitem_version_type": "AM"}]}, 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"dateValue": "2019-08-27"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "paper.pdf", "filesize": [{"value": "1.2 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 1200000.0, "url": {"label": "paper", "objectType": "fulltext", "url": "https://nagoya.repo.nii.ac.jp/record/27045/files/paper.pdf"}, "version_id": "b64e657f-3767-4fd2-b616-8f7237156880"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "Localization", "subitem_subject_scheme": "Other"}, {"subitem_subject": "failure detection", "subitem_subject_scheme": "Other"}, {"subitem_subject": "reliability", "subitem_subject_scheme": "Other"}, {"subitem_subject": "convolutional neural network", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Rao\u2013Blackwellized particle filter", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Simultaneous pose and reliability estimation using convolutional neural network and Rao\u2013Blackwellized particle filter", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Simultaneous pose and reliability estimation using convolutional neural network and Rao\u2013Blackwellized particle filter", "subitem_title_language": "en"}]}, "item_type_id": "10", "owner": "1", "path": ["1939/1940/1941"], "permalink_uri": "http://hdl.handle.net/2237/00029245", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2019-02-06"}, "publish_date": "2019-02-06", "publish_status": "0", "recid": "27045", "relation": {}, "relation_version_is_last": true, "title": ["Simultaneous pose and reliability estimation using convolutional neural network and Rao\u2013Blackwellized particle filter"], "weko_shared_id": -1}