Xueli Cheng
Papers
1
Total Citations
2
H-Index
1
About
Xueli Cheng is a researcher specializing in mobile robotics and multi-sensor fusion, with a particular focus on improving autonomous navigation and self-localization accuracy. Their most cited work introduces a novel self-localization method for mobile robots that addresses a critical limitation of classical Kalman filtering: the accumulation of errors over time during odometer-based dead reckoning. By implementing a limited memory Kalman filter with an exponential fading factor, Cheng’s approach effectively mitigates error retention, significantly enhancing localization precision in dynamic environments. This contribution has garnered attention in the field of robotics, with their key paper accumulating citations that underscore its relevance to advancing sensor fusion techniques. Cheng’s work is particularly valuable for researchers developing robust navigation systems for autonomous vehicles and service robots, offering a practical solution to a persistent challenge in real-time positioning. Their research bridges theoretical filtering methods with applied robotics, making it a useful reference for students and engineers working on multi-sensor integration and state estimation.
Research Focus
Key Achievements
Top Papers
- 1