Yasaman Haghighi
Papers
1
Total Citations
6
H-Index
1
About
Yasaman Haghighi is an emerging researcher working at the intersection of computer vision, robotics, and deep learning, with a particular focus on visual simultaneous localization and mapping (vSLAM) and neural implicit representations. Her most notable work, "Neural Implicit Dense Semantic SLAM" (2023), demonstrates her innovative approach to advancing autonomous navigation systems. In this research, she proposes a novel RGBD vSLAM framework that integrates semantic understanding with neural implicit scene representations, enabling robots to not only map unknown environments and localize themselves simultaneously, but to do so with richer, semantically meaningful spatial awareness. This contribution addresses a critical challenge in robotics — building accurate, interpretable maps in real time using only camera sensors — and positions her work at the forefront of modern SLAM research. Although early in her career, with the paper accumulating 6 citations since its publication, her contributions signal a promising trajectory in a rapidly evolving field. Researchers and students exploring intelligent robotic perception, 3D scene understanding, or neural rendering-based mapping will find her work a compelling and technically rigorous reference point.
Research Focus
Key Achievements
Top Papers
- 1Neural Implicit Dense Semantic SLAM6 citations · 2023