Miquel Campos is a control systems engineer and field robotics specialist with 14 years of experience building and software-driving underwater vehicles (ROVs/AUVs) using C++, Python and ROS. He blends hands‑on mechanical design and machining with advanced perception work—developing laser-camera sensors for 3D seafloor mapping during his PhD and improving visual odometry integrations such as viso2_ros. After a research trajectory at the University of the Balearic Islands and a senior research fellowship at the University of Southampton focused on ocean perception and remote imaging, he now applies that expertise at Ocean Infinity. Comfortable spanning low-level algorithms, middleware and hardware, he has also taught industrial automation and robotics modules and holds a Fellowship of the Higher Education Academy. Notably, his contributions to ROS visual odometry packages emphasize practical, production-ready fixes like point-cloud color integration and transform handling.
14 years of coding experience
2 years of employment as a software developer
Doctorado, Tecnología de la información, Doctorado, Tecnología de la información at Universitat de les Illes Balears
Fellow of the Higher Education Academy, Higher Education/Higher Education Administration, Fellow of the Higher Education Academy, Higher Education/Higher Education Administration at University of Southampton
A ROS wrapper for libviso2, a library for visual odometry
Role in this project:
Back-end Developer
Contributions:32 commits, 3 PRs, 4 pushes in 7 years 3 months
Contributions summary:Miquel primarily focused on enhancing the `viso2_ros` package, which serves as a ROS wrapper for visual odometry. Their contributions involved implementing and refining point cloud publishing functionality, integrating color information into the point cloud data, and fixing issues related to data processing. They also addressed compilation errors and improved the integration of transformation functionalities. The user demonstrated expertise in modifying C++ code within a ROS environment to improve and add features to visual odometry algorithms.
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