Dr Mehdi Hosseinzadeh
Postdoctoral Research Fellow (B)
School of Computer and Mathematical Sciences
Faculty of Sciences, Engineering and Technology
As a Research Fellow at the Australian Institute for Machine Learning, Mehdi contributes to projects at the intersection of Computer Vision, Machine Learning, and Robotics. His current focus lies in harnessing self-supervised multi-modal learning for advancements in robotics and autonomous driving. With a background in Geometry, Visual SLAM, 3D Reconstruction, and Multi-Modal Learning, his aim is to merge theoretical research with practical applications.
He earned his PhD from the University of Adelaide, where he was affiliated with the Australian Centre for Robotic Vision for Machine Learning. During his doctoral studies, Mehdi specialised in real-time structure and object-aware semantic visual SLAM. He tackled the challenges inherent in traditional geometric SLAM frameworks by integrating rich priors derived from cutting-edge object detections and advanced 3D scene understanding. For more information about SLAM and his work, refer to: https://www.adelaide.edu.au/aiml/our-research/robotic-vision/simultaneous-localisation-and-mapping-slam
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Conference Papers
Year Citation 2024 Garg, S., Rana, K., Hosseinzadeh, M., Mares, L., Sünderhauf, N., Dayoub, F., & Reid, I. (2024). RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 35 (pp. 4090-4097). Yokohama, Japan: IEEE.
DOI Scopus1 -
Preprint
Year Citation 2024 Hosseinzadeh, M., & Reid, I. (2024). BEVPose: Unveiling Scene Semantics through Pose-Guided Multi-Modal BEV
Alignment.
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