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FloorplanNet: Learning Topometric Floorplan Matching for Robot Localization | |
2023 | |
会议录名称 | PROCEEDINGS - IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION |
ISSN | 1050-4729 |
卷号 | 2023-May |
页码 | 6168-6174 |
发表状态 | 已发表 |
DOI | 10.1109/ICRA48891.2023.10160977 |
摘要 | Given a building floorplan, humans can localize themselves by matching the observation of the environment with the floorplan using geometric, semantic, and topological clues. Inspired by this insight, this paper proposes a learning- based topometric robot localization method FloorplanNet, which implements a match between a metric robot map and the potentially inaccurate building floorplan in nonuniform scales and different shapes by semantic information. The method uses a novel Graph Neural Network to learn descriptors of nodes from topometric graphs generated from the input maps. We demonstrate that our method can match the 3D point cloud sub-map generated by the robot during the SLAM process with the 2D map. Furthermore, we apply our map-matching algorithm for real-world robot localization. We evaluate our method on several publicly available real-world datasets. Even though our network is solely trained using simulation data, our method demonstrates high robustness and effectiveness in real- world indoor environments and outperforms the existing SOTA map-matching algorithms. We further develop a simulator that automatically creates and annotates the required training data to train our neural networks. The method and simulator are released at: https://github.com/fengdelin/FloorplanNet.git © 2023 IEEE. |
会议举办国 | UK |
关键词 | Graph neural networks Indoor positioning systems Robot applications Different shapes Floorplans Geometric semantics Localization method Map-matching algorithm Matchings Real-world Robot localization Robot maps Semantics Information Localization Map Matching Graph Neural network |
会议名称 | 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 |
会议地点 | London, United kingdom |
会议日期 | May 29, 2023 - June 2, 2023 |
学科门类 | 工学::计算机科学与技术(可授工学、理学学位) |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20233514632785 |
EI主题词 | Semantics |
EI分类号 | 723.4 Artificial Intelligence ; 731.6 Robot Applications |
原始文献类型 | Conference article (CA) |
来源库 | IEEE |
引用统计 | 正在获取...
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文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/325844 |
专题 | 信息科学与技术学院 信息科学与技术学院_PI研究组_Sören Schwertfeger组 信息科学与技术学院_硕士生 信息科学与技术学院_博士生 |
作者单位 | 1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Robotics and Auto-Driving Laboratory (RAL), Baidu Research |
第一作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Delin Feng,Zhenpeng He,Jiawei Hou,et al. FloorplanNet: Learning Topometric Floorplan Matching for Robot Localization[C]:Institute of Electrical and Electronics Engineers Inc.,2023:6168-6174. |
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