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About

Atanas Gotchev is a researcher specializing in computer vision, robotic perception, and calibration methodologies, with a particular focus on advancing the precision and reliability of robotic systems in complex environments. His most influential work centers on robot-world-hand–eye calibration, where his 2019 comparative study — garnering 76 citations — introduced two novel calibration methods and rigorously benchmarked them against six state-of-the-art approaches, offering the robotics community a definitive reference for solving this foundational geometric problem. Gotchev has also made notable contributions to visual SLAM research through the FinnForest dataset (2020, 33 citations), providing a challenging, unstructured natural environment benchmark that broadens testing beyond conventional urban scenarios for autonomous driving and forestry robotics. His work on multi-view camera pose estimation for robotic manipulators demonstrates an innovative exploitation of kinematic redundancy to improve accuracy in manipulation tasks. Further extending his expertise to high-stakes industrial applications, Gotchev has contributed to stereoscopic vision systems for remote handling in the ITER nuclear fusion project and developed robust pose estimation frameworks for teleoperated robots operating in harsh conditions — underscoring both the breadth and real-world impact of his research.