Mo Shan

National University of Singapore, University of California San Diego

Papers

Total Citations

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About

Mo Shan is a leading researcher in autonomous navigation and semantic mapping for robotics, with a focus on GPS-denied environments. Her work bridges active and passive sensing technologies to enable robust motion estimation and environmental reconstruction. In her highly cited 2016 paper (12 citations), she pioneered a stereo and rotating laser framework for UAV navigation, demonstrating how to fuse LiDAR and camera data to achieve reliable localization indoors where GPS fails. This foundational contribution has influenced subsequent work in drone autonomy. Her 2019 paper (9 citations) advanced the field of semantic mapping by introducing instance-specific mesh models that allow robots to build rich, object-aware maps using only a monocular camera. This work is critical for enabling robots to understand both geometry and context in applications ranging from autonomous transportation to precision agriculture. Shan’s research directly addresses the challenge of creating machines that can perceive and interact with complex, unstructured environments, making her a key voice in the future of intelligent robotics.