Research

My research focuses on robot perception, machine learning, sensor-based motion planning and control. What I love most is computer vision and its mathematics. 


RGBD and Stereo SLAM: volumetric reconstruction and 3D incremental segmentation 
PLVS is a real-time system which leverages sparse RGB-D and Stereo SLAM, volumetric mapping and 3D unsupervised incremental segmentation. PLVS stands for Points, Lines, Volumetric mapping, Segmentation. More details on this page

PLVS
    


Perception and motion planning  for UGVs
Point cloud segmentation and 3D mapping by using RGBD and LIDAR sensors 
• Sensor-based motion planning in challenging scenarios
Robot dynamics/kinematics learning and control

DortmundPhoenix Traversability


Learning Algorithms for UGVs
Online WiFi mapping by using Gaussian processes
• Algorithms for learning the model of UGVs dynamics/kinematics (Gaussian processes and neural networks)

  


Multi-robot systems
3D Patrolling with tracked UGVs
Coverage with an heterogeneous team of robots
• Exploration with mobile robots: the Multi-SRG Method


Computer vision and visual servoing for UAVs
During my experience in Selex ES MUAS (now Leonardo), I designed and developed advanced real-time applications and managed applied research projects for UAVs (CREX-BASIOBSPYBALL-B). In particular, one of these projects focused on near real-time 3D reconstruction from live video-stream. See more on my linkedin profile

ASIO_B


Computer vision and visual servoing for mobile robots
Intercepting a moving object with a mobile robot
Appearance-based nonholonomic navigation from a database of images

VisualInterceptionSoccer


Exploration of unknown environments
• With mobile robots: the SRT Method
• With a multi-robot system: the Multi-SRG Method
• With a general robotic system: the Sensor-based Exploration Tree

 SRG-sim    SET-3sensors-6+3


Robot navigation and mapping with limited sensing
In this work, we characterize the information space of a robot moving in the plane with limited sensing. The robot has a landmark detector, which provides the cyclic order of the landmarks around the robot, and it also has a touch sensor, that indicates when the robot is in contact with the environment boundary. The robot cannot measure any precise distances or angles, and does not have an odometer or a compass. We propose to characterize the information space associated with such robot through the swap cell decomposition. We show how to construct such decomposition through its dual, called the swap graph, using two kinds of feedback motion commands based on the landmarks sensed. See more in these papers
Learning Combinatorial Map Information from Permutations of Landmarks
Learning combinatorial information from alignments of landmarks
Using a Robot to Learn Geometric Information from Permutations of Landmarks

SwapCells

 


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