Xiaolin Sheng
Papers
1
Total Citations
3
H-Index
1
About
Xiaolin Sheng is a robotics researcher whose work centers on autonomous exploration, simultaneous localization and mapping (SLAM), and sensor fusion for mobile robots. Their most notable contribution addresses a fundamental challenge in robotics: efficient frontier-based exploration. Sheng proposed a novel method that synergistically combines data from a Kinect depth sensor and a laser scanner, overcoming the limitations of using each sensor independently for SLAM. This fusion approach enhances mapping accuracy and robustness in complex environments, enabling more reliable autonomous navigation. While their key paper, "Robotic autonomous exploration SLAM using combination of Kinect and laser scanner" (2017), has garnered 3 citations, it represents an early and practical step toward integrating low-cost depth cameras with traditional laser rangefinders—a direction that has since become influential in field robotics. Sheng’s work is particularly valuable for students and researchers interested in sensor integration, exploration strategies, and the practical deployment of SLAM systems on resource-constrained platforms.
Research Focus
Key Achievements
Top Papers
- 1