Peyman Kabiri

Iran University of Science and Technology, Nottingham Trent University

Papers

Total Citations

H-Index

About

Peyman Kabiri is a researcher whose work bridges computer vision, robotics, and machine intelligence. His key research areas include visual simultaneous localization and mapping (SLAM), augmented reality (AR), and robotic arm precision control. Kabiri’s most notable contribution is a novel feature-based approach for indoor monocular SLAM, published in 2018, which tackles the persistent challenge of accurate camera tracking and map construction in unknown environments—a critical problem for both autonomous navigation and AR applications. This work has garnered 9 citations, reflecting its relevance to advancing robust visual perception systems. Earlier in his career, Kabiri focused on compensating for robot arm flexibility and positioning inaccuracy using machine learning methods. His 1998 and 1999 papers introduced innovative error-compensation techniques that address the inherent imprecision of loosely coupled robotic structures, achieving positioning improvements in both 2D and 3D space. Though these foundational studies have fewer citations (3 and 2, respectively), they represent early efforts to apply machine intelligence to real-world robotic hardware challenges. Kabiri’s work demonstrates a sustained commitment to enhancing the reliability and accuracy of autonomous systems, from vision-based mapping to physical robot control.