WEKO3
アイテム
Deep Learning Methods for Semantic Segmentation of Dense 3D SLAM Maps
http://hdl.handle.net/10228/00008271
http://hdl.handle.net/10228/000082715fc83c89-39f8-4e95-a546-1e294a616a89
| 名前 / ファイル | ライセンス | アクション |
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sociorobo_11.pdf (328.7 kB)
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| アイテムタイプ | 会議発表論文 = Conference Paper(1) | |||||||||||
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| 公開日 | 2021-05-24 | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||||
| 資源タイプ | conference paper | |||||||||||
| タイトル | ||||||||||||
| タイトル | Deep Learning Methods for Semantic Segmentation of Dense 3D SLAM Maps | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 著者 |
Yingjian, Pei
× Yingjian, Pei× Chumkamon, Sakmongkon× 林, 英治
WEKO
30038
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Most real-time SLAM systems can only achieve semi-dense mapping, and the robot lacks specific knowledge of the mapping results, so it can only achieve simple positioning and obstacle avoidance, which may be used as an obstacle in the face of the target object to be grasped, thus affecting the realization of motion planning. The use of semantic segmentation in dense SLAM maps allows the robot to better understand the map information, distinguish the meaning of different blocks in the map by semantic labels, and achieve fast feature matching and Loop Closure Detection based on the relationship between semantic labels in the scene. There are many semantic segmentation datasets based on street scenes and indoor scenes available for use, and these datasets have some common tags. Based on these training data, we can derive a semantic segmentation model based on RGB images by using the Pytorch platform for training. | |||||||||||
| 備考 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | The 2021 International Conference on Artificial Life and Robotics (ICAROB 2021), January 21-24, 2021, Higashi-Hiroshima (オンライン開催に変更) | |||||||||||
| 書誌情報 |
Proceedings of International Conference on Artificial Life & Robotics (ICAROB2021) p. 764-767, 発行日 2021-01 |
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| 出版社 | ||||||||||||
| 出版社 | ALife Robotics | |||||||||||
| ISBN | ||||||||||||
| 識別子タイプ | ISBN | |||||||||||
| 関連識別子 | 978-4-9908350-6-4 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2435-9157 | |||||||||||
| 著作権関連情報 | ||||||||||||
| 権利情報 | Copyright (c) The 2021 International Conference on Artificial Life and Robotics (ICAROB2021) | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | 3D SLAM | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Semantic Segmentation | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Point Cloud | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | ROS | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| 査読の有無 | ||||||||||||
| 値 | yes | |||||||||||
| 連携ID | ||||||||||||
| 値 | 8852 | |||||||||||
