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About

Yimin Liu is an emerging robotics and autonomous systems researcher whose work centers on millimeter-wave (mmWave) radar sensing, mobile robot perception, and simultaneous localization and mapping (SLAM). His research addresses one of the most pressing challenges in autonomous navigation: enabling robust, reliable operation under adverse weather and visually degraded conditions where conventional optical sensors fail. Liu's most influential contribution, "A Novel Radar Point Cloud Generation Method for Robot Environment Perception" (2022, 96 citations), established new methods for leveraging mmWave radar data as a viable alternative to LiDAR and camera-based systems. Building on this foundation, he has developed sophisticated odometry and localization frameworks, including the radar-inertial odometry system DRIO (31 citations), which demonstrates strong performance in dynamic environments, and a mmWave-based relocalization approach for vision-impaired settings (16 citations). More recently, his multisensor fusion work—blending radar with monocular vision—and his transformer-based moving object segmentation network, RadarMOSEVE, highlight his expanding research scope. Collectively accumulating over 160 citations in just a few years, Liu's contributions are shaping how future autonomous robots perceive and navigate complex, real-world environments where traditional sensing approaches fall short.