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About

Naser El‐Sheimy is a leading researcher in autonomous navigation, mobile mapping, and intelligent perception systems, with a focus on enabling robust localization for autonomous vehicles, robotics, and smart city applications. His major contributions span visual and LiDAR-inertial SLAM, where he has developed innovative frameworks for real-time localization in dynamic and GPS-denied environments. Notably, his work on GAT-LSTM introduces a graph attention network for feature management in visual SLAM, significantly improving robustness in dynamic settings (9 citations). He has also advanced object-level SLAM by integrating shape-based tightly coupled IMU/camera systems, addressing non-Gaussian error distributions for more reliable robot-environment interactions. El‐Sheimy’s research on indoor mobile mapping using RGB-D sensors and stereo vision for moving obstacle detection has been pivotal for autonomous driving safety, with each paper garnering 5 citations. His improved stochastic modeling of low-cost GNSS receivers enhances positioning accuracy for UAVs and robotics, while his reference key frame algorithm optimizes SLAM for search-and-rescue operations. With a portfolio of highly cited work, El‐Sheimy’s contributions are shaping the future of autonomous navigation and 3D modeling, making him a key figure in the field.