Muhammad Latif Anjum
Seoul National University, National University of Sciences and Technology, Politecnico di Torino
Papers
10
Total Citations
131
H-Index
7
About
Muhammad Latif Anjum is a robotics and artificial intelligence researcher whose work centers on mobile robot localization, sensor fusion, visual SLAM, and human-robot interaction. His most significant contributions lie in developing robust frameworks that enable mobile robots to navigate and perceive their environments with greater accuracy and reliability. Anjum's early and most influential work focused on sensor data fusion techniques, particularly leveraging Unscented Kalman Filters (UKF) to achieve precise mobile robot localization — a paper that has garnered 39 citations and remains a cornerstone reference in the field. His complementary research on vision tracking systems, combining accelerometers, gyroscopes, encoders, and fuzzy logic controllers, further demonstrated his commitment to biologically inspired, multi-modal sensing approaches. More recently, his 2022 paper on Deep Introspective SLAM — earning 33 citations — showcases his evolution toward deep reinforcement learning methods for overcoming failure modes in visual SLAM systems, addressing one of robotics' most persistent challenges. Beyond localization, Anjum has made notable contributions to human activity recognition using skeleton joint tracking and intuitive robot navigation via sketch-based interfaces. With a career spanning foundational filter-based methods to cutting-edge deep learning approaches, his work reflects a sustained commitment to making autonomous robots smarter, more resilient, and more naturally interactive with humans.
Research Focus
Key Achievements
Top Papers
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- 6A sketch is worth a thousand navigational instructions7 citations · 2021
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- 9Scan Matching for Graph SLAM in Indoor Dynamic Scenarios4 citations · 2014
- 10Stable Vision System for Indoor Moving Robot Using Encoder Information4 citations · 2009