Liaisan Safarova

Papers

Total Citations

H-Index

About

Liaisan Safarova is a researcher in robotics and computer vision, with a primary focus on visual simultaneous localization and mapping (SLAM) systems. Her most notable work, "Comparison of Monocular ROS-Based Visual SLAM Methods" (2022), provides a critical evaluation of monocular SLAM algorithms operating within the Robot Operating System (ROS) framework. This study systematically benchmarks the performance, accuracy, and computational efficiency of leading visual SLAM methods, offering valuable guidance for researchers and engineers deploying autonomous navigation in resource-constrained environments. By highlighting the trade-offs between different approaches, Safarova’s contribution aids in the selection of optimal SLAM solutions for real-world robotic applications. While her work has garnered initial citations, it lays a strong foundation for advancing robust, lightweight mapping and localization techniques. Her research is particularly relevant to the development of autonomous vehicles, drones, and mobile robots, where reliable visual perception is critical. Safarova’s comparative analysis serves as a practical resource for the robotics community, underscoring her commitment to improving the accessibility and performance of SLAM technologies.