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About

Yanmin Wu is a robotics and computer vision researcher whose work sits at the intersection of simultaneous localization and mapping (SLAM), 3D scene understanding, and autonomous perception. His research focuses on enabling robots to interpret and interact with their environments at a semantic level, moving beyond geometric mapping toward richer, object-aware representations. Wu's most influential contribution is his Object SLAM framework, which addresses longstanding challenges in data association, object representation, and semantic mapping for robot high-level perception and decision-making — garnering nearly 50 citations since its 2023 publication. His work on BSH-Det3D advances LiDAR-based 3D object detection by tackling shape degradation in occluded and distant regions, directly improving performance in autonomous driving contexts. He has also contributed meaningfully to the relocalization problem, proposing a graph propagation-based semantic descriptor that improves robustness across varying lighting and viewpoint conditions. His most recent work on InstanceGaussian explores joint appearance-semantic representations using 3D Gaussian Splatting for instance-level scene perception. Collectively, Wu's research demonstrates a consistent drive to bridge low-level sensing with high-level robot reasoning, making him a noteworthy contributor to the growing field of semantic spatial intelligence.