Chang Qing Ren
Papers
1
Total Citations
4
H-Index
1
About
Chang Qing Ren is a researcher whose work lies at the intersection of mobile robotics, sensor fusion, and autonomous navigation. Ren’s most cited work, "Laser sensor based localization of mobile robot using Unscented Kalman Filter" (2016), addresses a fundamental challenge in robotics: precisely determining a robot’s position and orientation in a known environment. By integrating data from laser distance sensors and wheel encoders, Ren developed a method that leverages geometrical primitives—such as lines and polygons—to improve localization accuracy. This approach, employing the Unscented Kalman Filter, represents a significant contribution to robust state estimation for mobile platforms. While the paper has garnered 4 citations, its impact is notable for introducing a practical, sensor-fusion technique that balances computational efficiency with reliability. Ren’s work is especially relevant for researchers and students exploring real-world implementations of autonomous systems, offering a clear pathway from theoretical filtering methods to applied robotics. Through this research, Ren has helped advance the field of localization, providing tools that enhance the safety and precision of mobile robots in structured environments.
Research Focus
Key Achievements
Top Papers
- 1