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Dense Reconstruction from Visual SLAM with Probabilistic Multi-Sequence Merging

dc.contributor.authorZHANG, HANXIANG
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Applied Scienceen_US
dc.contributor.departmentDepartment of Electrical & Computer Engineeringen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinern/aen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr. Ya-Jun Panen_US
dc.contributor.thesis-readerDr. Kamal El-Sankaryen_US
dc.contributor.thesis-supervisorDr. Jason Guen_US
dc.date.accessioned2023-12-18T13:46:55Z
dc.date.available2023-12-18T13:46:55Z
dc.date.defence2023-12-13
dc.date.issued2023-12-14
dc.description.abstractThis thesis presents a comprehensive visual SLAM system that extends the application of ORB-SLAM3. Using it as a template, a supplementary and optional function of 3D dense reconstruction is implemented for both RGB-D and stereo cameras. With conventional datasets, TUM, EuRoC, and KITTI as benchmarks, we confirm the validity of proposed system in both indoor and outdoor scenarios. Besides, the concept of Octree is integrated into our system to generate Octomap. A compact mapping can be achieved as such, verified by the fact that the size of each dense point cloud map is reduced to approximately one-fifth after the conversion. Furthermore, a multi-sequence merging method is included in our proposed system, formulating with a probabilistic-based optimizing algorithm and map accessing functions from the original system. Multi-sequence experiments evince that the tracking accuracy profits from the exploitation of a priori knowledge gathered through the preceding sequences.en_US
dc.identifier.urihttp://hdl.handle.net/10222/83295
dc.language.isoenen_US
dc.subjectSLAMen_US
dc.subject3D Reconstructionen_US
dc.subjectOptimizationen_US
dc.titleDense Reconstruction from Visual SLAM with Probabilistic Multi-Sequence Mergingen_US

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