Yaqin Mao
Papers
1
Total Citations
2
H-Index
1
About
Yaqin Mao’s research lies at the intersection of computer vision, semantic scene understanding, and robotic perception, with a particular focus on enhancing visual SLAM (Simultaneous Localization and Mapping) systems. Her most-cited work, “DeepLabV3+-Based Semantic Annotation Refinement for SLAM in Indoor Environments” (2025), tackles a critical bottleneck in autonomous navigation: the inability of monocular SLAM to accurately reconstruct 3D scenes in semantically sparse environments. By integrating the DeepLabV3+ architecture for refined semantic segmentation, Mao’s approach significantly improves object-level mapping and robotic operational efficiency, offering a practical solution to the limitations of manual annotation methods. Though early in its citation trajectory, this work has already garnered attention for its innovative fusion of deep learning with classical SLAM pipelines. Mao’s contributions are particularly valuable for indoor robotics, where precise environmental understanding is essential for tasks like autonomous navigation and human-robot interaction. Her research underscores a growing trend toward hybrid systems that combine geometric mapping with semantic reasoning, positioning her as a promising voice in advancing robust, context-aware robotic perception.
Research Focus
Key Achievements
Top Papers
- 1