Parsa Yazdankhah
Papers
1
Total Citations
6
H-Index
1
About
Parsa Yazdankhah is a robotics researcher whose work centers on advancing autonomous navigation and perception for humanoid platforms. His major contributions lie in the rigorous evaluation and benchmarking of RGB-D SLAM (Simultaneous Localization and Mapping) algorithms, specifically tailored for the complex dynamics of bipedal locomotion. In his highly cited 2023 study, "Comparative Evaluation of RGB-D SLAM Methods for Humanoid Robot Localization and Mapping," Yazdankhah conducted a systematic comparison of three leading frameworks—RTAB-Map, ORB-SLAM3, and OpenVSLAM—using the SURENA-V humanoid robot. By having the robot traverse a full circular path while equipped with an Intel® RealSense depth camera, his work provided critical insights into the robustness and accuracy of these methods under real-world walking conditions. This research, which has garnered 6 citations, directly addresses the unique challenges of drift and sensor noise inherent to humanoid platforms, offering a practical guide for researchers selecting SLAM solutions. Yazdankhah’s work is a foundational reference for anyone developing reliable localization systems for legged robots, bridging the gap between classical SLAM theory and the demanding requirements of humanoid autonomy.
Research Focus
Key Achievements
Top Papers
- 1