Papers
7
Total Citations
85
H-Index
6
About
Kamal M. Othman is a leading researcher in social robotics and indoor scene understanding, with a focus on equipping autonomous robots with the perceptual intelligence to navigate and interact within human environments. His work bridges computer vision, deep learning, and control systems, addressing critical challenges in indoor scene classification, simultaneous localization and mapping (SLAM), and obstacle avoidance. Othman’s most impactful contributions include pioneering the use of convolutional neural networks (CNNs) like VGG16 and Inception V3 for room classification, achieving 19 citations, and developing a dual-stream deep learning framework for improved scene understanding in robotics (18 citations). He also advanced SLAM for humanoid robots by integrating laser and camera systems with closed-loop controllers, a method cited 17 times, and introduced a novel doorway detection and direction system using only a monocular camera (10 citations). His work on fractional order controllers for SLAM and fuzzy Q-learning for obstacle avoidance further underscores his versatility. With over 85 total citations across seven key papers, Othman’s research is foundational for creating socially aware robots capable of seamless indoor navigation and interaction.
Research Focus
Key Achievements
Top Papers
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- 5The Study of Fractional Order Controller with SLAM in the Humanoid Robot8 citations · 2014
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- 7Development of robotic rover with controller & vision system5 citations · 2020