Xinger Cheng
Papers
1
Total Citations
6
H-Index
1
About
Xinger Cheng is a researcher specializing in sensor fusion and perception systems, with a primary focus on the critical challenge of Camera-LiDAR calibration. Their most-cited work, "Camera LiDAR calibration: an automatic and accurate method with novel PLE metrics" (2024, 6 citations), introduces an innovative approach that enhances the precision and automation of aligning visual and depth data. This contribution is foundational for enabling robust sensor fusion in autonomous vehicles, robotics, and augmented reality, where accurate spatial understanding is paramount. By proposing novel Point-Line-Edge (PLE) metrics, Cheng addresses a key bottleneck in multi-modal perception, improving the reliability of systems that rely on synchronized camera and LiDAR inputs. Though early in their career, Cheng's work has already garnered attention for its practical impact, offering a streamlined solution that reduces manual calibration effort while boosting accuracy. Their research stands out for bridging theoretical calibration methods with real-world deployment needs, making it a valuable reference for engineers and researchers advancing autonomous navigation and 3D scene understanding.
Research Focus
Key Achievements
Top Papers
- 1