Shuzlina Abdul-Rahman
Papers
8
Total Citations
117
H-Index
6
About
Dr. Shuzlina Abdul-Rahman is a leading researcher at the forefront of autonomous systems, specializing in Simultaneous Localization and Mapping (SLAM), object detection, and sensor fusion for autonomous vehicles and mobile robots. Her seminal survey on SLAM methods in autonomous vehicles (57 citations) has become a foundational reference for researchers exploring self-driving car technologies, particularly highlighting the critical role of LiDAR and deep learning in perception systems. Dr. Abdul-Rahman’s work on object detection using LiDAR with deep learning (21 citations) addresses the limitations of camera-based systems in low-light conditions, advancing robust perception for autonomous navigation. She has also pioneered simulation-based approaches for SLAM using 3D point cloud data, enabling more accessible algorithm development. Her recent contributions include improving Mask R-CNN algorithms for partial occlusion detection, enhancing industrial robot grasping accuracy. With over 100 cumulative citations, Dr. Abdul-Rahman’s research bridges theoretical advances and practical implementations, making her a key figure in the evolution of intelligent autonomous systems. Her work continues to shape how robots perceive and navigate complex environments.
Research Focus
Key Achievements
Top Papers
- 1A Brief Survey on SLAM Methods in Autonomous Vehicle57 citations · 2018
- 2Object Detection for Autonomous Vehicle with LiDAR Using Deep Learning21 citations · 2020
- 3A review on object detection for autonomous mobile robot10 citations · 2023
- 4
- 5Visual analytics of 3D LiDAR point clouds in robotics operating systems8 citations · 2020
- 6
- 7PARTIAL OCCLUSION OBJECT DETECTION BASED ON IMPROVED MASK-RCNN3 citations · 2024
- 8