Electronic Engineer · Robotics & Automation
Electronic Engineer and PhD in Systems Engineering and Computer Science (supervised by Prof. Javier Civera), with international experience across industrial automation, autonomous robotics, oilfield services and advanced manufacturing. I develop robust, real-time and deployable systems by combining research in SLAM, sensor fusion and state estimation with hands-on engineering experience in C++, Python, MATLAB and PLC-based automation.
What I work on
Perception and estimation for robust autonomous systems.
Control and integration for industrial vehicles and machinery.
Deployable software designed around performance and reliability.
From research to deployment
ACelli
Developing and validating AGV control, navigation and safety functions using Siemens PLCs, CAN/CANopen and industrial sensor systems.
KUKA
Worked on Gaussian-based 3D reconstruction using an Intel RealSense camera and a KUKA LBR iisy cobot. Developed ROS 2 tools for data recording, synchronization and alignment.
Hydroner
Contributed to the development and deployment of wireless communication systems for IoT-based monitoring in oilfield environments.
Matra SRL
Designed and implemented automated control systems for clients in the oil industry using Allen-Bradley and Siemens PLCs.
Selected research
A novel dataset designed to benchmark methods in the intersection between SLAM and novel view rendering. It consists of 40 sequences with synchronized RGB, depth, IMU, robot kinematic data, and ground-truth pose streams.
A new deformable visual-inertial odometry framework that separates a rigid, IMU-anchored state from a non-rigid warp represented by an embedded deformation graph.
A new analytical solution that is easy to implement and robust at initialization, thanks to the small-rotation and constant-velocity approximations, which simplify the problem while preserving the essential coupling between motion and inertial measurements.
A novel GNSS-inertial initialization strategy that delays the use of global GNSS measurements until sufficient information is available to accurately estimate the state of a sensorized device. A criterion based on the evolution of the Hessian matrix singular values is introduced.
Estimates camera motion and IMU state in a rigid 3D environment, with the flexibility to operate as a multiframe optimization or to marginalize older data.
Earlier work
Applies compressive sensing to obtain photocurrent maps of photovoltaic devices without mechanical scanning.