Ammar Zakaria
Papers
19
Total Citations
247
H-Index
9
About
Ammar Zakaria is a robotics and autonomous systems researcher whose work spans mobile robotics, simultaneous localization and mapping (SLAM), mobile olfaction, and sensor fusion. He is perhaps best known for his pioneering investigations into adapting low-cost depth sensors for robotic navigation — most notably his work demonstrating how Microsoft Kinect could effectively replace expensive laser scanners in SLAM frameworks such as Gmapping and Hector SLAM, papers that have collectively attracted over 70 citations. A distinctive thread running through his research is the fusion of spatial mapping with gas sensing: his 2018 work integrating SLAM with gas distribution mapping (SLAM-GDM) for real-time gas source localization represents a meaningful step toward robots capable of autonomously detecting hazardous substances in complex environments. Zakaria has also explored metal oxide gas sensor characterization, multi-sensor data fusion using Principal Component Analysis, and more recently reinforcement learning approaches for path planning in unknown environments. His development of behaviour-based robots equipped with both olfactory and visual sensing reflects a broader ambition to build richly perception-capable autonomous systems. With a diverse body of work spanning over a decade, Zakaria offers valuable insights for researchers working at the intersection of robotics, sensing, and intelligent navigation.
Research Focus
Key Achievements
Top Papers
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- 3Method to convert Kinect's 3D depth data to a 2D map for indoor SLAM31 citations · 2013
- 4Implementation of Behaviour Based Robot with Sense of Smell and Sight17 citations · 2015
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- 8Mobile robot localization system using multiple ceiling mounted cameras12 citations · 2015
- 9Gas Source Localization using Grey Wolf Optimizer9 citations · 2018
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