Yulin Tan

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About

Yulin Tan is a leading researcher in robotics and autonomous navigation, with a focus on advancing LIDAR-based simultaneous localization and mapping (SLAM) for real-world applications. His most cited work, "Low-cost and High-accuracy LIDAR SLAM for Large Outdoor Scenarios" (2019, 14 citations), introduces a breakthrough algorithm that enables high-precision mapping and localization using lightweight, affordable hardware. By leveraging LIDAR’s superior accuracy and range over vision sensors, Tan’s approach overcomes critical challenges in large-scale outdoor environments, making reliable autonomous navigation more accessible. This contribution is particularly impactful for field robotics, including agricultural, mining, and search-and-rescue operations. Tan’s research demonstrates a rare ability to balance cost-efficiency with performance, pushing the boundaries of practical SLAM systems. His work has garnered attention for its potential to democratize high-accuracy navigation technology, offering a scalable solution for industries requiring robust autonomy. Through innovative algorithm design and hardware optimization, Yulin Tan continues to shape the future of real-time, low-cost robotic perception and mapping.