Mohamed Mustafa

Papers

Total Citations

H-Index

About

Mohamed Mustafa’s research centers on robotics perception, motion estimation, and 3D computer vision, with a particular focus on developing mathematically rigorous methods for autonomous navigation. His most influential work, “Guaranteed SLAM—An interval approach” (2017, 31 citations), introduces a novel framework for Simultaneous Localization and Mapping (SLAM) that uses interval analysis to provide guaranteed, bounded estimates of robot pose and map features—a critical advance for safety-critical applications where uncertainty must be explicitly quantified. This approach stands out for its ability to handle sensor noise and data association ambiguities without relying on probabilistic assumptions. In related work, Mustafa explored rigid transformation estimation using interval analysis for robot motion (2015, 5 citations), offering a robust alternative to traditional least-squares methods when correspondences from range sensors are noisy or incomplete. He also contributed to 3D feature detection and description (2015, 2 citations) by fusing SURF features from RGB images with geometric information from structured-light sensors, enabling more reliable point cloud matching. Though his citation counts are modest, Mustafa’s emphasis on guaranteed, interval-based methods represents a distinctive and principled contribution to the SLAM and robotics community.