Papers
22
Total Citations
623
H-Index
11
About
Muqing Cao is a robotics researcher whose work spans autonomous aerial systems, sensor fusion, multi-robot coordination, and simultaneous localization and mapping (SLAM). His most influential contributions center on enabling robots to perceive and navigate complex environments without relying on external infrastructure such as GPS. Cao's landmark achievement is the NTU VIRAL dataset (2021, 175 citations), a richly multi-modal benchmark combining visual, inertial, ranging, and lidar data from aerial platforms — a resource that has meaningfully accelerated autonomous aerial research worldwide. Complementing this, his VIRAL-Fusion framework (2021, 102 citations) demonstrated how tightly coupling these diverse sensor modalities can overcome persistent challenges like estimation drift and degraded performance in low-texture environments. Beyond state estimation, Cao has made notable strides in multi-robot systems, developing persistently excited adaptive methods for relative localization and formation control of robot swarms (2019, 70 citations), and addressing the intricate problem of trajectory planning for multiple tethered UAVs through his NEPTUNE framework (2023, 39 citations). His more recent work on optimality-aware LiDAR-inertial odometry and heterogeneous UAV inspection benchmarks reflects a maturing research agenda pushing toward practical, deployable robotic systems. Collectively, his publications have garnered over 500 citations, establishing him as a rising force in aerial robotics and autonomous navigation.
Research Focus
Key Achievements
Top Papers
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- 2VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach102 citations · 2021
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- 8Relative Localizability and Localization for Multirobot Systems14 citations · 2025
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