UAVs · Sensor Fusion · Control · ROS 2 · SLAM · Machine Learning

I work on robotics and autonomous systems, with a strong focus on drones, state estimation, optimal control, and perception pipelines.
My projects combine theoretical foundations (control, estimation, optimization) with practical ROS 2 implementations and simulation environments.

• UAV trajectory planning & control

• Optimal control and MPC-based approaches for quadrotors

• Constraint-aware planning for real-world dynamics

• Sensor fusion & state estimation

• Extended Kalman Filters (EKF)

• Multi-sensor fusion (IMU, GPS, magnetometer, barometer)

• Robotics algorithms

• Localization, odometry, and control

• Ground and aerial robot applications

• ROS 2 & simulation

• Custom ROS 2 Humble environments

• Gazebo-based testing and integration

• Machine learning

• Neural networks and deep learning (academic & applied projects)

• Visual SLAM & VIO

• Stereo / RGB-D pipelines

• Visual odometry and mapping

Drone-optimal-trajectory
Optimal trajectory planning through MPC for UAVs using ROS 2, Gazebo, PX4. This project is in development phase. ➡️ https://github.com/Relo02/Drone-optimal-trajectory

Quadcopter-Sensor-Fusion
EKF-based sensor fusion framework for quadrotor state estimation using multiple onboard sensors.
➡️ https://github.com/Relo02/Quadcopter-Sensor-Fusion

Robotics_Project
Robotics fundamentals applied to localization, odometry, and control (ground robot use case).
➡️ https://github.com/Relo02/Robotics_Project

Ros-2-Environment
ROS 2 Humble simulation environment for UAV control system experimentation with obstacle awareness for enabling smart landing proceedure.
➡️ https://github.com/FALCOdrone/Ros-2-Environment

Artificial-neural-networks-and-deep-learning-
Academic and practical work on neural networks and deep learning concepts. ➡️ https://github.com/Relo02/Artificial-neural-networks-and-deep-learning-

Visual-Slam
Visual SLAM and VIO pipelines using stereo / RGB-D sensors for pose estimation and mapping. This project is in development phase. ➡️ https://github.com/palingenesys/Visual-Slam

• Middleware: ROS 2 (Humble), Gazebo
• Control & Planning: MPC, optimal control, trajectory optimization
• Estimation & Perception: EKF, Visual SLAM, VIO
• Sensors: IMU, GPS, LiDAR, stereo cameras
• ML/DL: Neural networks, deep learning

1. Model the system dynamics and constraints
2. Design robust state estimation via sensor fusion
3. Plan feasible, optimal, solver efficient trajectories
4. Integrate everything cleanly in ROS 2
5. Validate in simulation before real deployment

• Email: ortolore@gmail.com
• Linkedin: https://www.linkedin.com/in/lorenzo-ortolani-6135b7240/

⭐ I enjoy turning robotic systems and complex autonomous software into reality.