Papers
1
Total Citations
4
H-Index
1
About
Yuanhao Qu is a researcher specializing in sensor fusion, 3D perception, and autonomous navigation, with a particular focus on integrating LiDAR and visual data for robust environmental mapping. His most cited work, "A Scan-to-Locality Map Strategy for 2D LiDAR and RGB-D Data Fusion" (2021), introduces an innovative approach that bridges the gap between sparse LiDAR scans and dense RGB-D imagery. By aligning 2D LiDAR data with locality-based maps from RGB-D sensors, Qu’s method enhances localization accuracy in complex, feature-poor environments—a critical challenge for mobile robotics and autonomous systems. This contribution has garnered attention within the robotics community, with the paper accumulating 4 citations to date, reflecting its relevance in advancing real-time perception pipelines. Qu’s research underscores the importance of multi-modal data integration, offering practical solutions for improving SLAM (Simultaneous Localization and Mapping) performance. His work is particularly valuable for researchers exploring cost-effective sensor fusion strategies, as it demonstrates how combining low-cost 2D LiDAR with RGB-D cameras can achieve high-fidelity spatial understanding without relying on expensive 3D LiDAR units.
Research Focus
Key Achievements
Top Papers
- 1A Scan-to-Locality Map Strategy for 2D LiDAR and RGB-D Data Fusion4 citations · 2021