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An Improved Indoor Map Construction Method Based on Millimeter-Wave Radar

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成果类型:
会议论文
作者:
Li, Fei;Wang, Xinfu;Li, Shuo*;Gu, Xin;Gao, Kai;...
通讯作者:
Li, Shuo
作者机构:
[Wang, Xinfu; Qin, Aina; Gu, Xin; Li, Fei; Huang, Zhiwu] Cent South Univ, Sch Automat, Changsha, Peoples R China.
[Li, Shuo] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China.
[Gao, Kai] Changsha Univ Sci & Technol, Coll Automot & Mech Engn, Changsha, Peoples R China.
通讯机构:
[Li, Shuo] C
Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China.
语种:
英文
关键词:
Indoor Map Construction;Material Classify;Convolutional Neural Networks;Space Feature Extraction
期刊:
2021 7TH INTERNATIONAL CONFERENCE ON AUTOMATION, ROBOTICS AND APPLICATIONS (ICARA 2021)
年:
2021
页码:
253-257
会议名称:
7th International Conference on Automation, Robotics and Applications (ICARA)
会议时间:
FEB 04-06, 2021
会议地点:
ELECTR NETWORK
会议主办单位:
[Li, Fei;Wang, Xinfu;Gu, Xin;Qin, Aina;Huang, Zhiwu] Cent South Univ, Sch Automat, Changsha, Peoples R China.^[Li, Shuo] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China.^[Gao, Kai] Changsha Univ Sci & Technol, Coll Automot & Mech Engn, Changsha, Peoples R China.
会议赞助商:
IEEE, IEEE Robot & Automat Soc, Beijing Inst Control Robot & Intelligence Technol, Czech Univ Life Sci Prague
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-6654-0469-3
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61672537]
机构署名:
本校为通讯机构
院系归属:
汽车与机械工程学院
电气与信息工程学院
摘要:
Aimed at enhancing accuracy and enriching se-mantic features of indoor map construction which is used for path planning of robot, this paper proposes a novel method based on extracting features from point clouds which come from millimeter-wave radar, combined convolutional neural networks. Data obtained from millimeter-wave radar are divided into two parts. One part extracts line segments and edge points as features of point clouds. The features build similar index is used to represent similarity between two point clouds to choose optimal transform matrix which transforms one point cloud into ...

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