Arman Karimian

iRobot (United States), Boston University

Papers

Total Citations

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About

Arman Karimian is a roboticist whose research lies at the intersection of long-term autonomy, state estimation, and perception. His work addresses fundamental challenges in enabling robots to operate reliably in the real world over extended periods. Karimian’s most significant contributions include pioneering strategies for lifelong mapping in the wild, where he developed novel methods to ensure map stability and accuracy over time—a critical problem validated on thousands of robots. He has also advanced the field of pose graph optimization through his work on rotational outlier identification using dual decomposition, which improves the robustness of SLAM (Simultaneous Localization and Mapping) systems. Additionally, Karimian has tackled the challenging problem of bearing-only navigation under field-of-view constraints, introducing novel navigation vector fields for visual homing when only directional measurements are available. His papers have garnered citations from the robotics community, reflecting their impact on practical autonomous navigation. Karimian’s research is particularly notable for its focus on real-world deployment and scalability, making his work essential reading for students and researchers interested in robust, long-term robotic autonomy and perception.