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.
Hello, I'm Samuel Cerezo
Expertise
Autonomous Robotics
SLAM, visual-inertial odometry, sensor fusion, state estimation
Industrial Automation
AGVs, Siemens PLCs, TIA Portal, CAN/CANopen, industrial safety
Software and Systems
C++, Python, MATLAB, ROS 2, optimization, real-time systems
Selected Experience
Automation Engineer · ACelli
Developing and validating AGV control, navigation and safety functions using Siemens PLCs, CAN/CANopen and industrial sensor systems.
Jan 2026 - Present
Porcari, Italy
Research Intern · 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.
Apr 2024 - Mar 2025
Augsburg, Germany
Junior Engineer · Hydroner
Contributed to the development and deployment of wireless communication systems for IoT-based monitoring in oilfield environments.
Jun 2020 - Feb 2021
Neuquén, Argentina
Junior Automation Engineer · Matra SRL
Designed and implemented automated control systems for clients in the oil industry using Allen-Bradley and Siemens PLCs.
2019 - 2020
Neuquén, Argentina
Publications
SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM
IROS, 2026
New
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.
DefVINS: Visual-Inertial Odometry for Deformable Scenes
Submitted to IEEE ICRA 2027
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.
An Efficient Closed-Form Solution to Full Visual-Inertial State Initialization
IEEE RA-L, 2026
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.
GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residuals
IEEE RA-L, 2025
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.
Camera Motion Estimation from RGB-D-inertial Scene Flow
IEEE/CVF CVPR Workshop, 2024
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.
Master's Thesis
Compressive Sensing Mapping System for Spatial Characterization of Photovoltaic Devices
Argentine Conference on Electronics
Applies compressive sensing to obtain photocurrent maps of photovoltaic devices without mechanical scanning.