Mikel Etxeberria Garcia

Papers

Total Citations

H-Index

About

Mikel Etxeberria Garcia is a researcher whose work sits at the intersection of computer vision and robotics, with a particular focus on improving how autonomous systems perceive and navigate their environments. His primary research areas include visual odometry, deep learning for image enhancement, and robust perception for mobile robots and drones. His most notable contribution is the development of a novel approach that leverages Generative Adversarial Networks (GANs) for image enhancement to improve monocular visual odometry performance. This work, published in 2021, directly addresses a critical limitation of state-of-the-art methods like ORB-SLAM and DF-VO, which struggle in challenging, low-light, or adverse weather conditions. By using GANs to synthesize clearer, more informative frames, Etxeberria Garcia’s method enables more reliable trajectory estimation and localization for autonomous vehicles and drones. While his work is still early in its citation lifecycle, its practical relevance to real-world deployment of autonomous systems—from warehouse robots to outdoor drones—marks him as an emerging voice in the field, with clear potential for significant future impact.