Papers
3
Total Citations
21
H-Index
3
About
Adlane Habed is a leading researcher in computer vision and robotics, specializing in sensor fusion, camera calibration, and motion estimation. His work is central to enabling robust visual odometry and SLAM in challenging outdoor environments, where accurate camera motion is critical. His most cited paper, "2D-3D camera fusion for visual odometry in outdoor environments" (2014, 12 citations), pioneers the integration of 2D and 3D cameras—common on modern robots—to refine motion estimates through nonlinear optimization, directly improving the reliability of autonomous navigation. Habed also advances fundamental calibration techniques, including self-calibration of non-rotating zooming cameras (6 citations) and a novel approach to Hand-Eye calibration (2019, 3 citations). The latter tackles the persistent problem of estimating Euclidean transformations between rigidly attached frames, addressing real-world issues like synchronization errors and hardware noise through an iteratively re-weighted rank-constrained semi-definite programming method. His contributions provide practical, high-accuracy solutions for robotics and augmented reality, bridging theoretical rigor with real-world deployment. With a focus on robust, self-calibrating systems, Habed’s work continues to shape the future of autonomous perception.
Research Focus
Key Achievements
Top Papers
- 12D-3D camera fusion for visual odometry in outdoor environments12 citations · 2014
- 2Self-calibration of stationary non-rotating zooming cameras6 citations · 2014
- 3