User:MatteoMatteucci

From AIRWiki

Jump to: navigation, search
Matteo Matteucci
Foto di MatteoMatteucci
E-Mail: matteo.matteucci@polimi.it
Research Areas:

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:

Enjoy the reading!



Contents

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
ARTag.jpg

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
Autodiff.png

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

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
VisualOdometry.jpg

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

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
SofwareEingineer.jpg

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
PointCloudKinect.jpg

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
Quadrotor.jpg

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

Machine Learning, Soft Computing & Pattern Recognition

List of ongoing project and thesis

Ongoing thesis


Long term research activities


PhD Students I am currently tutoring


PhD Students I have tutored


Projects I am currently tutoring


Past tutored projects


Past tutored students