Jon Zubieta Ansorregi
Papers
1
Total Citations
3
H-Index
1
About
Jon Zubieta Ansorregi is a researcher focused on computer vision and robotics, with particular expertise in visual odometry (VO) for autonomous systems. His work addresses critical challenges in enabling drones, mobile robots, and autonomous vehicles to navigate complex environments using monocular cameras. Zubieta Ansorregi’s most cited paper, "Image Enhancement using GANs for Monocular Visual Odometry" (2021), tackles the limitations of state-of-the-art VO techniques like ORB-SLAM and DF-VO, which perform well outdoors but struggle in challenging conditions. By leveraging generative adversarial networks (GANs) for image enhancement, his approach improves robustness and accuracy in low-light or texture-poor scenarios—a significant contribution to real-world deployment. While his citation count is still growing (3 citations for this work), the research demonstrates a promising intersection of deep learning and robotics. Zubieta Ansorregi’s work is particularly relevant for students and researchers interested in practical applications of GANs, autonomous navigation, and the ongoing effort to bridge the gap between lab performance and field-ready systems.
Research Focus
Key Achievements
Top Papers
- 1Image Enhancement using GANs for Monocular Visual Odometry3 citations · 2021