Mina Henein

Australian National University, Australian Centre for Robotic Vision

Papers

Total Citations

131

H-Index

About

Mina Henein is a robotics researcher specializing in autonomous systems, Simultaneous Localisation and Mapping (SLAM), and visual odometry — fields that sit at the heart of modern robot navigation and perception. Her most significant contribution, VDO-SLAM (2020), has garnered over 110 citations and represents a landmark advancement in dynamic environment modeling. By integrating SLAM estimation with dynamic object awareness, her work addresses a critical limitation of classical SLAM systems, which traditionally assume a static world — an assumption that breaks down in real-world scenarios involving moving vehicles, pedestrians, and other dynamic obstacles. This has direct implications for robot path planning and obstacle avoidance in complex, unpredictable environments. Beyond VDO-SLAM, Henein has explored how meta-structural information can improve SLAM consistency and has investigated equivariant frameworks for visual odometry, leveraging symmetry principles to develop more robust navigation algorithms. Her earlier work on SLAM with dynamic rigid objects further demonstrates a sustained commitment to bridging theoretical robotic estimation with practical autonomy challenges. Across her career, Henein has established herself as an innovative voice in making autonomous robots more capable of operating safely and reliably in the messy, dynamic real world.