User:MatteoMatteucci

Matteo Matteucci
E-Mail:	matteucc@elet.polimi.it
Research Areas:	Affective Computing BioSignal Analysis Computer Vision and Image Analysis Machine Learning Robotics

This is my home page on the airwiki website. Here you can find projects and thesis proposals together with references to (PhD) students I am tutoring or I've been tutoring in the past.

You will NOT find here my research statements, my teaching material, my publications, and so on. If you are looking for something is not here you can try:

• My Personal home page (more complete and updated)
• My page at DEI (official home page)

Enjoy the reading!

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List of project and thesis proposals

Almost all the topics proposed here can be tacled at different levels from simple course projects to master thesis and sometimes even up to an entire PhD thesis. This is the reason for having several classifications in the additional info ;-)

Robotics & Computer Vision

Wiki Page: Accurate AR Marker Location Title: C++ Library for accurate marker location based on subsequent pnp refinements Description: ARTags, QR codes, Data Matrix, are visual landmark used for augmented reality, but they could be used for robotics as well. A thesis has already been done on using data matrix for robot localization and mapping, but improvements are required in terms generality, accuracy and robustness of the solution. The goal is thuss to: increase the number of markers supported by the system (ARTag + QR codes) increase the accuracy of the detection and localization of the marker test different algorithms for the solution of the perspective from n points problem Material: papers on PnP algorithms, OpenCV, Matlab code with three PnP algorithms implementations C++ libraries for marker detection (to be found and evaluated) Expected outcome: C++ library to the robust localization of artificial markers a ROS node performing accurate ARTag localization a comparison of Tags and algorithms in a real world scenario The use of this library in a SLAM framework (Thesis) Required skills or skills to be acquired: background on computer vision and image processing C++ programming under Linux Tutor: MatteoMatteucci Additional Info: CFU 5 - 10 / Bachelor of Science, Master of Science / Thesis, Course

Wiki Page: Automatic Differentiation Techniques for Real Time Kalman Filtering Title: Evaluation of Automatic Differentiation Techniques for Gauss-Newton based Simultaneous Localization and Mapping Description: In Gauss-Newton non linear optimization one of the most tedious part is computing Jacobians. At the AIRLab we have developed a framework for non linear Simultaneous Localization and Mapping suitable for different motion models and measurement equations, but any time you need to change something you need to recompute the required Jacobian. Automatic differentiation is a tool for the automatic differentiation of source code either at compiling time or at runtime; we are interested in testing these techniques in the software we have developed and compare their performance with respect to (cumbersome) optimized computation. Material C++ modules for Extended Kalman Filtering libraries for automatic differentiation (http://www.autodiff.org/) Expected outcome: New modules implementations based on automatic differentiation A comparison between the old stuff and new approach Required skills or skills to be acquired: C++ programming under Linux Tutor: MatteoMatteucci Additional Info: CFU 10 - 20 / Master of Science / Thesis

Wiki Page: Comparison of State of the Art Visual Odometry Systems Title: A Comparison of State of the Art Visual Odometry Systems (Monocular and Stereo) Description: Visual odometry is the estimation of camera(s) movement from a sequence of images. In case we deal with a single camera system we have Monocular Visual Odometry; in case we have more cameras we have a Stero Visual Odometry. The goal of the thesis is to review the state of the art on in visual odometry, classify existing approaches and compare their implementations (many of the algorithms have online source code available). Material a huge corpus of literature on the topic libraries on visual odometry (http://www.cvlibs.net/datasets/kitti/index.php) C++ library for image processing and computer vision (OpenCV) Expected outcome: a set of running algorithms performing visual odometry Required skills or skills to be acquired: computer vision and 3D reconstruction C++ programming under Linux Tutor: MatteoMatteucci Additional Info: CFU 10 - 20 / Bachelor of Science, Master of Science / Thesis, Course

Wiki Page: MoonSLAM Reengineering Title: Reengineering of a flexible framework for simultaneous localization and mapping Description: In the last three years a general framework for the implementation of EKF-SLAM algorithm has been developed at the AIRLab. After several improvements it is now time to redesign it based on the experience cumulated. The goal is to have an international reference framework for the development of EKF based SLAM algorithms with multiple sensors (e.g., lasers, odometers, inertial measurement ) and different motion models (e.g., free 6DoF motion, planar motion, ackerman kinematic, and do on). The basic idea is to implement it by using C++ templates, numerically stable techniques for Kalman filtering and investigation the use of automatic differentiation. It should be possible to seamlessly exchange motion model and sensor model without having to write code beside the motion model and the measurement equation. Material lots of theoretical background and material an existing (and working) C++ implementation of the framework Expected outcome: a C++ library for the implementation of generic EKF-SLAM algorithms Required skills: Experienced C++ programming under Linux Tutor: MatteoMatteucci Additional Info: CFU 20 - 20 / Master of Science / Thesis

Wiki Page: Poit cloud SLAM with Microsoft Kinect Title: Point cloud SLAM with Microsoft Kinect Description: Simultaneous Localization and Mapping (SLAM) is one of the basic functionalities required from an autonomous robot. In the past we have developed a framework for building SLAM algorithm based on the use of the Extended Kalman Filter and vision sensors. A recently available vision sensor which has tremendous potential for autonomous robots is the Microsoft Kinect RGB-D sensor. The thesis aims at the integration of the Kinect sensor in the framework developed for the development of a point cloud base system for SLAM. Material: Kinect sensor and libraries A framework for multisensor SLAM PCL2.0 library for dealing with point clouds Expected outcome: Algorithm able to build 3D point cloud representation of the observed scene Point clouds processing could be used to improve the accuracy of the filter as well Required skills or skills to be acquired: Basic background in computer vision Basic background in Kalman filtering C++ programming under Linux Tutor: MatteoMatteucci Additional Info: CFU 10 - 20 / Master of Science / Thesis

Wiki Page: Unmanned Aerial Vehicles Visual Navigation Title: A critical review on the state of the art in visual navigation for unmanned aerial vehicles Description: Visual navigation is becoming more and more important in the development of unmanned aerial vehicles (UAV). The goal of this thesis/tesina is to review in a structured way the current state of art in the field from different perspective: research teams, projects, platforms, tasks, algorithms. The latter is the most important aspect obviously and the project should provide a clear view on what is done today, and obtaining which results. Two kind of operations are of most interest: tracking of fixed and mobile targets (and how this impact on the UAV path), navigation on a geo-referenced map. Implementing one of the standard approaches on a mini unmanned aerial vehicle would be the ideal ending of the work to turn it into a thesis. Material: papers from major journals and conferences reports from research projects Expected outcome: a report with a detailed review of the state of the art organized according to the main relevant aspects (to be identified during the work) an implementation of some state of the art algorithms for tracking or navigation Required skills or skills to be acquired: proficiency in english basic understanding of computer vision basic understanding of filtering techniques Tutor: MatteoMatteucci Additional Info: CFU 10 - 20 / Bachelor of Science, Master of Science / Thesis

List of ongoing project and thesis

• AGW (Control system design of an electric wheelchair for autonomous drive with obstacle avoidance MauroGabellone EugenioCeravolo)
• Deep Learning on Event-Based Cameras (This project aims to study deep learning techniques on event-based cameras and develop algorithms to perform object recognition on those devices. MarcoCannici)
• DiffDrivePlanner (A Search-Based Trajectory Planner for differential drive vehicles in ROS Context VitoRessa)
• HRVCar (DarioSortino FrancescoTogninalli Bruno Romano)
• I.DRIVE Data Logger (This project concerns the logging of data coming from multiple sensor installed in a car. This data are used to analyze the behaviours and the reactions of the driver AlessandroGabrielli)
• Low-cost IMU (Characterization of a low-cost IMU module developed by AIRLab; use of such module to extract high-level information about human behavior. NguyenHo)
• Object Recognition with Deep Boltzmann Machines (Deep Boltzmann machines for classification tasks CarloDEramo)
• RoboCom+R2P (The goal of the project is to develop a Robot equipped with collision avoidance logic. ValerioArcerito PietroBalatti AlessandroCianferotti)
• SLDR (LorenzoPorro)
• Sensor fusion for autonomous cars (This project concerns the logging and use of the data fusion coming from multiple sensor installed in a car. The data are used to generate the decisions and behavior of an autonomous car. LuisSierra)
• Sound source localization for robots (SimonePradolini)
• SprayinWithBrain (AI and Robotics in agriculture LorenzoFedeli Simone1Parisi LuigiBonoBonacchi)
• Triskar+R2P (The goal of the project is to build an omnidirectional robot based on existing hardware. AndreaCavalli LucaAgostini)

PhD Students I am currently tutoring

• AndreaSemprebon
• MarcoCannici
• SimoneMentasti

PhD Students I have tutored

• BernardoDalSeno
• DavideCucci
• DavideMigliore
• FrancescoVisin
• LuigiMalago
• RossellaBlatt
• SimoneCeriani

Projects I am currently tutoring

• AGW (Control system design of an electric wheelchair for autonomous drive with obstacle avoidance MauroGabellone EugenioCeravolo)
• Deep Learning on Event-Based Cameras (This project aims to study deep learning techniques on event-based cameras and develop algorithms to perform object recognition on those devices. MarcoCannici)
• DiffDrivePlanner (A Search-Based Trajectory Planner for differential drive vehicles in ROS Context VitoRessa)
• HRVCar (DarioSortino FrancescoTogninalli Bruno Romano)
• I.DRIVE Data Logger (This project concerns the logging of data coming from multiple sensor installed in a car. This data are used to analyze the behaviours and the reactions of the driver AlessandroGabrielli)
• Low-cost IMU (Characterization of a low-cost IMU module developed by AIRLab; use of such module to extract high-level information about human behavior. NguyenHo)
• Object Recognition with Deep Boltzmann Machines (Deep Boltzmann machines for classification tasks CarloDEramo)
• RoboCom+R2P (The goal of the project is to develop a Robot equipped with collision avoidance logic. ValerioArcerito PietroBalatti AlessandroCianferotti)
• SLDR (LorenzoPorro)
• Sensor fusion for autonomous cars (This project concerns the logging and use of the data fusion coming from multiple sensor installed in a car. The data are used to generate the decisions and behavior of an autonomous car. LuisSierra)
• Sound source localization for robots (SimonePradolini)
• SprayinWithBrain (AI and Robotics in agriculture LorenzoFedeli Simone1Parisi LuigiBonoBonacchi)
• Triskar+R2P (The goal of the project is to build an omnidirectional robot based on existing hardware. AndreaCavalli LucaAgostini)

Past tutored projects

• BCI & artifacts (DarioRusignuolo)
• BCI based on Motor Imagery (FabioZennaro)
• BCI on Sockets (MarioPolino NikoZarzani)
• Balancing Robot Development (MattiaCrippa MarcoLattarulo MichelePersiani)
• Characterization of the NIA signal (GiulioValenti)
• Extraction (FabioMarfia)
• Gestures in Videogames (GiorgioPrini)
• HeadsetControlForWheelChair (RobertoVandone)
• Indoor localization system based on a gyro and visual passive markers (DarioCecchetto LorenzoConsolaro)
• Integration of scanSLAM and ARToolKit in the MoonSLAM framework (MatteoLuperto MladenMazuran)
• Interpretation of facial expressions and movements of the head (CristianMandelli)
• Machine Learning for Crop Weed Classification (LodewijkVoorhoeve)
• R2P (AndreaZoppi)
• Recognition of the user's focusing on the stimulation matrix (LeonardoVolpe)
• Robot Localization and Navigation With Visual Markers (AndreaPremarini AndreaScalise)
• Stimulus tagging using aperiodic visual stimulation in a VEP-based BCI (DavideCastellone GiuseppeBroccio)
• Triskar (AndreaSorbelli AlessandroDeangelis)

Past tutored students

• AlfredoMotta
• AndreaCampana
• AndreaPremarini
• AndreaScalise
• AndreaSgarlata
• AndreaSorbelli
• AntonioTripodi
• BernardoDalSeno
• ClaudioSesto
• DanielaMazzeo
• DarioCecchetto
• DarioRusignuolo
• DavideCastellone
• DavideCucci
• DavideMigliore
• DiegoConsolaro
• EleonoraCiceri
• EmanueleCorsano
• ErmesViviani
• FabioBeltramini
• … further results

ALL PROJECTS ABOUT MYSELF

Open Proposals

• Accurate AR Marker Location
• Automatic Differentiation Techniques for Real Time Kalman Filtering
• Comparison of State of the Art Visual Odometry Systems
• MoonSLAM Reengineering
• Poit cloud SLAM with Microsoft Kinect
• Unmanned Aerial Vehicles Visual Navigation

Projects

• AGW
• Ackerman vehicle autonomous drive
• BCI & artifacts
• BCI based on Motor Imagery
• BCI on Sockets
• Balancing Robot Development
• Characterization of the NIA signal
• Deep Learning on Event-Based Cameras
• DiffDrivePlanner
• Extraction
• Gestures in Videogames
• HRVCar
• HeadsetControlForWheelChair
• I.DRIVE Data Logger
• Indoor localization system based on a gyro and visual passive markers
• Integration of scanSLAM and ARToolKit in the MoonSLAM framework
• Interpretation of facial expressions and movements of the head
• Low-cost IMU
• Machine Learning for Crop Weed Classification
• Object Recognition with Deep Boltzmann Machines
• … further results

Closed Proposals

• Aperiodic visual stimulation in a VEP-based BCI
• Automatic generation of domain ontologies
• Behavior recognition from visual data
• Combinatorial optimization based on stochastic relaxation
• Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization
• Creation of new EEG training by introduction of noise
• Driving an autonomous wheelchair with a P300-based BCI
• Exploratory data analysis by genetic feature extraction
• Extended Kalman Filtering on Manifolds
• Information geometry and machine learning
• LARS and LASSO in non Euclidean Spaces
• Multimodal GUI for driving an autonomous wheelchair
• Odometric system for robots based on laser mice
• P300 BCI
• R2P IMU firmware development
• Real-time removal of ocular artifact from EEG
• Robocentric MoonSLAM
• Scan Matching Odometry and Multisensor SLAM
• Self calibration of multiple odometric sensors
• Statistical inference for phylogenetic trees
• … further results