Search Results for author: Abdulla Ayyad

Found 6 papers, 3 papers with code

A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environment

1 code implementation13 Feb 2023 Xiaoqian Huang, Kachole Sanket, Abdulla Ayyad, Fariborz Baghaei Naeini, Dimitrios Makris, Yahya Zweiri

To the best of our knowledge, this densely annotated and 3D spatial-temporal event-based segmentation benchmark of tabletop objects is the first of its kind.

Benchmarking Segmentation +1

A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions

no code implementations23 Jun 2022 Mohammed Salah, Mohammed Chehadah, Muhammed Humais, Mohammed Wahbah, Abdulla Ayyad, Rana Azzam, Lakmal Seneviratne, Yahya Zweiri

Driven by this necessity, this work proposes a robust relative localization system based on a fusion of neuromorphic vision-based measurements (NVBMs) and inertial measurements.

Landmark Tracking

Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers

1 code implementation17 Dec 2021 Yusra Alkendi, Rana Azzam, Abdulla Ayyad, Sajid Javed, Lakmal Seneviratne, Yahya Zweiri

Compared to existing solutions, qualitative results verified the superior capability of the proposed algorithm to eliminate noise while preserving meaningful scene events.

Denoising

Multirotors from Takeoff to Real-Time Full Identification Using the Modified Relay Feedback Test and Deep Neural Networks

no code implementations6 Oct 2020 Abdulla Ayyad, Mohamad Chehadeh, Pedro Silva, Mohamad Wahbah, Oussama Abdul Hay, Igor Boiko, Yahya Zweiri

Low cost real-time identification of multirotor unmanned aerial vehicle (UAV) dynamics is an active area of research supported by the surge in demand and emerging application domains.

Neuromorphic Eye-in-Hand Visual Servoing

no code implementations15 Apr 2020 Rajkumar Muthusamy, Abdulla Ayyad, Mohamad Halwani, Yahya Zweiri, Dongming Gan, Lakmal Seneviratne

Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in spatio-temporal space.

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