Xuanlai Tang
Papers
1
Total Citations
23
H-Index
1
About
Dr. Xuanlai Tang is a leading researcher in robotics and autonomous systems, with a primary focus on cross-modal perception and visual localization for intelligent vehicles. His most impactful work addresses a critical challenge in mobile robotics: achieving reliable, low-cost localization by aligning monocular camera images with pre-existing LiDAR maps. In his highly cited 2023 paper, "Cross-Modal Monocular Localization in Prior LiDAR Maps Utilizing Semantic Consistency," Dr. Tang proposes a novel framework that leverages semantic consistency to establish stable correspondences between 2D images and 3D LiDAR point clouds. This work, which has already garnered 23 citations, overcomes a key instability in cross-modal matching, enabling high-accuracy localization without the need for expensive LiDAR sensors on the vehicle. By bridging the gap between visual and geometric data, Dr. Tang’s contributions are paving the way for more practical and scalable autonomous navigation systems. His research is instrumental for students and engineers working on robust, sensor-efficient solutions for self-driving cars and mobile robots.
Research Focus
Key Achievements
Top Papers
- 1