3D semantic segmentation using camera-lidar sensor fusion for autonomous driving / Khalid Mohamed Naguib Elmadawi ; Supervised Hanan Ahmed Kamal , Omar Ahmed Nasr

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Khalid Mohamed Naguib Elmadawi

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TextLanguage: English Publication details: Cairo : Khalid Mohamed Naguib Elmadawi , 2021Description: 79 P. : charts , facsimiles ; 30cmOther title:

التقسيم الجزئى ثلاثى الأبعادب استخدام دمج مستشعر الكامير او الليدار للقيادة الذاتية [Added title page title]

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Issued also as CD

Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communication Summary: This research improves the environment sensing in control systems through multi-sensor fusion. We are fusing Camera raw image data with LiDAR Point cloud resulting from having a colored point cloud, represented in a spherical grid map representation. We used the fused data in early and mid-level fusion algorithms. We evaluate our algorithms on the KITTI dataset, which provides semantic segmentation for different classes such as Cars, Pedestrians, and cyclists. We evaluated our work on two states of art architectures, namely Squeeze Seg and Point Seg, resulting in improving the mIOU score to 10% in both cases relative to the LiDAR only baseline

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Item type	Current library	Home library	Call number	Copy number	Status	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.08.M.Sc.2021.Kh.T (Browse shelf(Opens below))		Not for loan	01010110085045000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.08.M.Sc.2021.Kh.T (Browse shelf(Opens below))	85045.CD	Not for loan	01020110085045000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communication

This research improves the environment sensing in control systems through multi-sensor fusion. We are fusing Camera raw image data with LiDAR Point cloud resulting from having a colored point cloud, represented in a spherical grid map representation. We used the fused data in early and mid-level fusion algorithms. We evaluate our algorithms on the KITTI dataset, which provides semantic segmentation for different classes such as Cars, Pedestrians, and cyclists. We evaluated our work on two states of art architectures, namely Squeeze Seg and Point Seg, resulting in improving the mIOU score to 10% in both cases relative to the LiDAR only baseline

Issued also as CD

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