Difference between revisions of "Computer Vision and Image Analysis"

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

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

Title: Mesh Refinement with Deep Learning
Description: Mesh Refinement with Deep Learning
Tutor: AndreaRomanoni
Additional Info: CFU 5 / Master of Science / Thesis

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