Joining LINEACT at CESI for a research internship would be a fantastic opportunity to contribute to innovative projects while deepening my skills in a cutting-edge environment focused on digital transformation and Industry 4.
This M2 internship is part of ongoing research on improving camera calibration techniques, focusing on optimizing datasets for intrinsic and extrinsic calibration in robotic manipulation contexts. Accurate camera calibration is essential for achieving precise perception and control in robotics, particularly in scenarios involving cameras mounted on robotic manipulators. Camera calibration is a critical step in computer vision applications, enabling accurate transformation between image and real-world coordinates.
However, the performance of calibration algorithms can vary significantly depending on the datasets used. Previous studies carried on our laboratory highlight that dataset quality and diversity play a vital role in ensuring robust calibration results, especially in dynamic environments like industrial robotics.
This internship will explore the disparity in calibration results across different datasets and investigate methods for creating optimized datasets tailored to robotic manipulators.
Analyse the variability of intrinsic and extrinsic camera calibration results when using different datasets. Propose and implement an optimized methodology for creating calibration datasets, considering factors such as pattern design, robot pose diversity, and environmental conditions.
