Joris Belhadj

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About

Joris Belhadj is a leading researcher in autonomous robotic navigation, with a focus on visual odometry (VO) for GPS-denied environments such as planetary terrains. His work bridges the gap between traditional frame-based cameras and emerging event-based sensors, which excel in low-light and high-speed motion scenarios. In his highly cited 2024 paper, "Deep Visual Odometry with Events and Frames," Belhadj pioneered a model-based approach that fuses data from both camera types, significantly improving robustness in challenging conditions. This contribution has already garnered 12 citations, reflecting its immediate impact on the field. By addressing the limitations of conventional VO systems—which often fail in dynamic lighting or rapid motion—Belhadj’s research advances the reliability of autonomous exploration for space missions and other extreme environments. His innovative integration of deep learning with sensor fusion positions him as a key figure in the next generation of robotic perception systems.