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About

Minglang Tan is a researcher specializing in robot perception, simultaneous localization and mapping (SLAM), and computer vision, with a particular focus on developing robust state estimation systems for autonomous robots. His work bridges classical probabilistic robotics with modern deep learning approaches, making significant contributions to both fields. Tan's most influential research centers on visual SLAM systems that fuse multiple sensor modalities. His 2019 paper on tightly-coupled monocular visual-odometric SLAM — integrating wheel odometry and MEMS gyroscopes — has garnered 70 citations, establishing him as a notable voice in the ground robotics localization community. Complementing this, his map-assisted EKF-based visual-inertial SLAM approach demonstrated how accurate, real-time motion tracking could be achieved even on standard CPUs, earning 32 citations and highlighting his commitment to practical, deployable solutions. More recently, Tan has extended his expertise into deep learning-driven depth estimation, with his DEFOM-Stereo framework leveraging monocular depth foundation models to enhance stereo matching — already accumulating 22 citations across related publications since 2025. This trajectory reflects a researcher continuously evolving at the intersection of classical robotics and modern AI, offering students a compelling model of interdisciplinary innovation in autonomous systems research.