Xiaoyu Long
Papers
1
Total Citations
9
H-Index
1
About
Xiaoyu Long is a researcher whose work centers on sensor fusion for autonomous systems, with a particular focus on depth estimation—a critical technology for autonomous driving and robot navigation. Long’s major contribution lies in developing robust and accurate methods that overcome the inherent limitations of single-sensor approaches. By fusing LiDAR and stereo camera data, Long has pioneered techniques that achieve superior depth perception, balancing the precision of LiDAR with the dense, contextual information from stereo vision. This work, detailed in the highly cited 2023 paper “Robust and accurate depth estimation by fusing LiDAR and stereo” (9 citations), addresses a fundamental challenge in real-world perception: ensuring reliability under varying environmental conditions. The impact of Long’s research is evident in its direct relevance to the safety and performance of autonomous vehicles, where accurate depth estimation is non-negotiable. Long’s contributions represent a significant step toward more resilient and practical perception systems, marking them as a rising voice in the field of autonomous navigation and sensor integration.
Research Focus
Key Achievements
Top Papers
- 1Robust and accurate depth estimation by fusing LiDAR and stereo9 citations · 2023