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dc.contributor.authorCheng, Lei
dc.contributor.authorCao, Siyang
dc.date.accessioned2024-01-25T17:54:10Z
dc.date.available2024-01-25T17:54:10Z
dc.date.issued2023-08-28
dc.identifier.citationCheng, L., & Cao, S. (2023, August). Online Targetless Radar-Camera Extrinsic Calibration Based on the Common Features of Radar and Camera. In NAECON 2023-IEEE National Aerospace and Electronics Conference (pp. 294-299). IEEE.en_US
dc.identifier.isbn979-835033878-2
dc.identifier.doi10.1109/naecon58068.2023.10366051
dc.identifier.urihttp://hdl.handle.net/10150/670763
dc.description.abstractSensor fusion is essential for autonomous driving and autonomous robots, and radar-camera fusion systems have gained popularity due to their complementary sensing capabilities. However, accurate calibration between these two sensors is crucial to ensure effective fusion and improve overall system performance. Calibration involves intrinsic and extrinsic calibration, with the latter being particularly important for achieving accurate sensor fusion. Unfortunately, many target-based calibration methods require complex operating procedures and well-designed experimental conditions, posing challenges for researchers attempting to reproduce the results. To address this issue, we introduce a novel approach that leverages deep learning to extract a common feature from raw radar data (i.e., Range-Doppler-Angle data) and camera images. Instead of explicitly representing these common features, our method implicitly utilizes these common features to match identical objects from both data sources. Specifically, the extracted common feature serves as an example to demonstrate an online targetless calibration method between the radar and camera systems. The estimation of the extrinsic transformation matrix is achieved through this feature-based approach. To enhance the accuracy and robustness of the calibration, we apply the RANSAC and Levenberg-Marquardt (LM) nonlinear optimization algorithm for deriving the matrix. Our experiments in the real world demonstrate the effectiveness and accuracy of our proposed method.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights©2023 IEEE.en_US
dc.rights.urihttps://rightsstatements.org/vocab/InC/1.0/en_US
dc.sourceNAECON 2023 - IEEE National Aerospace and Electronics Conference
dc.subjectcommon featuresen_US
dc.subjectextrinsic calibrationen_US
dc.subjectradaren_US
dc.subjectradar-camera calibrationen_US
dc.subjectsensor fusionen_US
dc.titleOnline Targetless Radar-Camera Extrinsic Calibration Based on the Common Features of Radar and Cameraen_US
dc.typeArticleen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineering, University of Arizonaen_US
dc.identifier.journalProceedings of the IEEE National Aerospace Electronics Conference, NAECONen_US
dc.description.noteImmediate accessen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
refterms.dateFOA2024-01-25T17:54:12Z


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