Papers
1
Total Citations
12
H-Index
1
About
Xinye Ma is a researcher specializing in robust localization and sensor fusion for autonomous land vehicles, with a focus on lidar-inertial odometry in challenging environments. Their most notable contribution is the development of LTI-SAM (Lidar-Template Matching-Inertial Odometry via Smoothing and Mapping), a novel framework that integrates lidar, template matching, and inertial measurements to overcome the limitations of traditional laser odometry in feature-poor or repetitive scenes. This work directly addresses critical failures in corridors, tunnels, airports, and mines—environments where conventional algorithms degrade or collapse entirely. With 12 citations since its 2022 publication, LTI-SAM has already garnered attention for its practical impact on real-world autonomous navigation. Ma’s research bridges the gap between theoretical SLAM approaches and deployment-ready solutions, offering robust localization where it is most needed. Their work is particularly valuable for students and engineers tackling autonomous systems in industrial or infrastructure settings, demonstrating how sensor fusion can achieve reliability in the most demanding conditions.
Research Focus
Key Achievements
Top Papers
- 1LTI-SAM: Lidar-Template Matching-Inertial Odometry via Smoothing and Mapping12 citations · 2022