Muhammad Haniff Gusrial
Papers
1
Total Citations
2
H-Index
1
About
Muhammad Haniff Gusrial is a robotics researcher whose work centers on advancing autonomous navigation through simultaneous localization and mapping (SLAM). His most-cited paper, "Review of Kalman filter variants for SLAM in mobile robotics with linearization and covariance initialization" (2025), provides a comprehensive synthesis of Kalman filter adaptations—including linearization techniques and covariance initialization strategies—that are critical for robust state estimation in unknown environments. This review has already garnered 2 citations, reflecting its timely relevance to the SLAM community. Gusrial’s contributions help bridge theoretical foundations and practical implementation, offering clear guidance for researchers and engineers tackling real-world robotics challenges. By systematically analyzing filter variants, his work supports the development of more reliable autonomous systems, from indoor mobile robots to outdoor exploration platforms. His research is particularly valuable for students and practitioners seeking to understand the trade-offs in SLAM algorithm design. With a focus on foundational yet impactful reviews, Gusrial is establishing himself as a thoughtful contributor to the field of mobile robotics and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1