Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7394
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dc.contributor.authorLo, Sheung-Laien_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLeung, Yeeen_US
dc.date.accessioned2023-02-20T11:56:52Z-
dc.date.available2023-02-20T11:56:52Z-
dc.date.issued2021-
dc.identifier.citation2021 6th International Conference on Control and Robotics Engineering (ICCRE), 2021, pp. 91-95en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7394-
dc.description.abstractA multiple sensors platform is a commonly-used method for robotic application which can capture different types of environmental data. However, the calibration process through finding the geometric relationships is needed in order to integrate different sensors' data together in the same coordination system representation. In this study, we proposed and implemented a simple and yet accurate calibration method for estimating the extrinsic parameters in a transformation matrix representation between more than one sensor with 3D Point-Cloud data. Our calibration method uses both Kinect and LiDAR Point-Cloud scans based on a cuboid as a calibration object, which obtains efficient information with high-level feature points. Such extrinsic parameters can be distinguished with a single shot of capturing the Point-Cloud frame. To verify whether or not our method is practical in a real-life scenario, we evaluated the method and obtained the data using a homemade robot. With our proposed calibration methods, it enables a possibility to exchange the data between LiDAR and Kinect simply and robustly.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.titleEvaluating the LiDAR and Kinect Calibration Methods and Applicationen_US
dc.typePeer Reviewed Journal Articleen_US
dc.relation.conference6th International Conference on Control and Robotics Engineering (ICCRE)en_US
dc.identifier.doi10.1109/ICCRE51898.2021.9435662.-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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