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About

Mingxing Wen is a robotics researcher whose work spans collaborative multi-robot systems, 3D mapping, sensor fusion, and autonomous localization in challenging environments. His research addresses some of the most complex problems in mobile robotics, including how teams of robots can collectively build accurate, semantically rich maps of unstructured and dynamic environments using heterogeneous sensor arrays. Wen's most influential contributions center on hierarchical probabilistic frameworks for fusing 3D occupancy maps across multiple robots in real-time distributed systems, with his foundational 2018 paper accumulating 44 citations. His subsequent work on multilevel sensor fusion and day-night collaborative dynamic mapping — each garnering over 40 citations — demonstrates a sustained focus on making multi-robot systems robust across diverse operational conditions. A notable technical achievement is his two-step extrinsic calibration method for sparse LiDAR and thermal camera systems, addressing a critical gap in sensor integration for resource-constrained platforms. Wen has also made significant strides in tackling localization within geometrically repetitive and symmetric environments — notoriously difficult scenarios for conventional SLAM approaches — proposing infrastructure-free and magnetic-assisted solutions that reduce dependency on pre-deployed systems. His collaborative semantic perception work further bridges geometry and contextual understanding for robot teams. Collectively, his publications reflect a cohesive research vision: enabling intelligent, cooperative autonomy in real-world, complex environments.