Raseeda Hamzah

Papers

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About

Raseeda Hamzah is a researcher at the forefront of autonomous vehicle navigation, with a primary focus on Simultaneous Localization and Mapping (SLAM) and 3D LiDAR point cloud analytics. Her work addresses one of the most critical challenges in self-driving technology: enabling vehicles to understand and map their environment in real time. Her most cited paper, "A Brief Survey on SLAM Methods in Autonomous Vehicle" (2018, 57 citations), provides a foundational overview of SLAM techniques for autonomous cars, particularly following the rise of commercial semi-autonomous vehicles like Tesla. Hamzah has also made significant contributions through simulation-based research, as demonstrated in "Simulation of simultaneous localization and mapping using 3D point cloud data" (2019, 8 citations), where she developed methods to simplify algorithm learning using LiDAR technology. Her work in "Visual analytics of 3D LiDAR point clouds in robotics operating systems" (2020, 8 citations) further advances the field by addressing the challenge of processing and visualizing complex point cloud data in robotics environments. Through her research, Hamzah has established herself as a key contributor to the practical implementation of SLAM systems, bridging the gap between theoretical algorithms and real-world autonomous vehicle applications.