Papers
1
Total Citations
10
H-Index
1
About
Yongfeng Su is a researcher in robotics and artificial intelligence, with a primary focus on simultaneous localization and mapping (SLAM) and deep learning applications for autonomous systems. His most cited work, "A novel relocation method for simultaneous localization and mapping based on deep learning algorithm" (2017), introduces an innovative approach that leverages deep learning to enhance the robustness of SLAM systems during robot relocation—a critical challenge in environments where visual features are degraded or repetitive. This contribution has garnered 10 citations, reflecting its early impact on the intersection of neural networks and geometric mapping. Su’s research addresses fundamental problems in mobile robotics, aiming to improve the reliability of autonomous navigation in complex, real-world settings. His work is particularly notable for bridging traditional SLAM techniques with modern deep learning architectures, offering a pathway toward more adaptive and resilient robotic perception. As a researcher, Su continues to explore how data-driven methods can solve long-standing challenges in spatial intelligence, making his contributions valuable for students and practitioners advancing autonomous systems.
Research Focus
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Top Papers
- 1