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About

Can Xu is a leading researcher in robotics and autonomous systems, with a primary focus on robust simultaneous localization and mapping (SLAM) for challenging, real-world environments. His most significant contribution is the creation of the SubT-MRS Dataset, a groundbreaking resource designed to push SLAM algorithms toward reliable performance in all-weather and subterranean conditions. This work directly addresses a critical gap in the field: the failure of current SLAM solutions to maintain resilience in degraded visual environments. Since its release in 2024, the dataset has already garnered 46 citations, reflecting its immediate impact on the research community. Xu’s efforts are pivotal for advancing autonomous navigation in GPS-denied areas, with applications ranging from search-and-rescue operations to planetary exploration. By systematically benchmarking SLAM under extreme lighting, dust, and thermal variations, he is helping to set new standards for robustness in robotics. His work not only provides a vital tool for researchers but also underscores the importance of bridging the gap between controlled laboratory conditions and the unpredictable demands of the real world.