Papers
2
Total Citations
29
H-Index
2
About
Etienne Grossmann is a researcher whose work lies at the intersection of computer vision and robotics, with a particular focus on motion estimation, egomotion, and omnidirectional perception. His most cited paper, “Camera self orientation and docking maneuver using normal flow” (1995, 24 citations), introduces a pioneering method for guiding a camera-equipped robot to dock with a stationary target by leveraging first-order approximations of optic flow. This contribution is notable for its practical approach to real-time visual servoing, enabling a six-degree-of-freedom robotic arm to orient itself using only minimal visual information—a concept that remains relevant in autonomous navigation and manipulation. Grossmann’s later work, “Toward Robot Perception through Omnidirectional Vision” (2007, 5 citations), expands his exploration into wide-field sensing, addressing challenges in scene understanding and robot localization. While his citation counts are modest, they reflect focused, foundational contributions to visual motion analysis and robotic perception. Grossmann’s research is particularly valuable for students and engineers interested in efficient, biologically inspired vision algorithms for robotics, demonstrating how sparse visual cues can drive complex behaviors in constrained environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Toward Robot Perception through Omnidirectional Vision5 citations · 2007