Yang Jin
Papers
1
Total Citations
2
H-Index
1
About
Yang Jin is a robotics researcher whose work focuses on advancing LiDAR-based localization and odometry for autonomous systems. His key contributions lie in addressing fundamental challenges in robot perception, particularly motion distortion and ranging errors that plague existing LiDAR odometry methods. In his highly influential paper "CTA-LO: Accurate and Robust LiDAR Odometry Using Continuous-Time Adaptive Estimation" (2024), Jin introduced a novel continuous-time framework that overcomes the limitations of traditional constant-velocity motion assumptions. This work, already garnering 2 citations shortly after publication, demonstrates his ability to develop theoretically rigorous yet practically impactful solutions for real-world robotic navigation. By enabling more precise and robust localization in dynamic environments, Jin's research directly advances the reliability of autonomous vehicles, drones, and mobile robots. His approach represents a significant step forward in making LiDAR-based systems more resilient to challenging conditions, positioning him as an emerging leader in the field of robotic perception and state estimation.
Research Focus
Key Achievements
Top Papers
- 1