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About

Binbin Xu is a robotics and computer vision researcher whose work spans simultaneous localization and mapping (SLAM), autonomous exploration, dense 3D reconstruction, and human-robot interaction. His most influential contribution is MID-Fusion, an octree-based object-level multi-instance dynamic SLAM system that enables robust camera tracking in dynamic environments while simultaneously estimating geometric, semantic, and motion properties of individual objects — a landmark advance in scene understanding that has accumulated over 225 citations across its publications. Building on this object-centric philosophy, Xu extended his work to autonomous aerial systems, developing semantic exploration frameworks for micro aerial vehicles that move beyond simple geometric mapping toward context-aware discovery of specific objects in unknown environments. His 2023 work on incremental dense reconstruction from monocular video using guided sparse feature volumes further demonstrates his commitment to practical, efficient 3D perception for robotics and augmented reality. Earlier contributions include spherical image completion for robot perception and foundational work in human-robot synchronous control via skeletal tracking. Xu has also addressed the practical challenge of accurate visual-inertial sensor calibration through intelligent trajectory suggestion. Collectively, his research reflects a coherent vision: enabling robots to perceive, understand, and interact with dynamic, complex environments with greater intelligence and reliability.