I. Asl Sabbaghian Hokmabadi

Papers

Total Citations

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About

I. Asl Sabbaghian Hokmabadi is a researcher advancing the frontiers of autonomous navigation and spatial intelligence, with a primary focus on LiDAR-inertial localization, mapping, and sensor fusion for robotics and smart city applications. Their most cited work, "LiDAR-Inertial Localization with Ground Constraint in a Point Cloud Map" (2023, 5 citations), introduces a novel framework that integrates LiDAR and inertial data with ground plane constraints to achieve robust, real-time trajectory estimation within pre-existing point cloud maps—a critical capability for autonomous vehicles and mobile robots operating in GPS-denied environments. This contribution addresses the fundamental challenge of accurate, drift-free localization by leveraging geometric constraints to enhance stability and precision. Hokmabadi’s research bridges the gap between theoretical sensor fusion and practical deployment, offering scalable solutions for dynamic urban settings. With a growing citation footprint, their work is gaining recognition among engineers and researchers seeking to improve the reliability of autonomous systems. By combining rigorous algorithmic design with real-world applicability, Hokmabadi is helping to shape the next generation of intelligent, self-localizing machines, making their profile essential reading for anyone interested in the future of autonomous navigation and spatial computing.