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- R2P IMU firmware development (Embedded Inertial Measurement Unit for Unmanned Aerial Vehihcles We have developed the electronics of an Inertial Measurement Unit based on an ARM microcontroller to be integrated on an autonomous embedded aerial platform. The IMU has already some attitude heading reference system (AHRS) code implemented, but we are interested in:
- implementing embedded algorithms for the estimation of the IMU attitude to be compared with the actual one (e.g., Kalman filter, DCM, Madgwick, etc.)
- developing a, easy to use, procedure for the calibration of IMU parameters
- making a comparison with commercial units using a robot arm as testbed
- validate the accuracy of the IMU on a flying platform
- integrate the measurements from a GPS to reduce drift and provide accurate positiong (this will make it definitely a MS thesis)
Material
- electronic board and eclipse based C development toolkit for ARM processors
- papers describing the algorithms we are interested in implementing
Expected outcome:
- few different AHRS algorithms with comparative results
- user-friendly procedure to calibrate the IMU
- a sistem which integrated IMU and GPS to provide accurate positioning
Required skills or skills to be acquired:
- C programming on ARM microcontroller
- background on kalman filtering and attitude estimation)
Real-time removal of ocular artifact from EEG (Real-time removal of ocular artifact from EEG In a Brain-Computer Interface (BCI) based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.
Robocentric MoonSLAM (Robocentric implementation in the MoonSLAM framework 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. The actual implementation of the EKF SLAM in the framework developed uses a world-centric approach, but from the literature it is known that a robocentric approach can provide higher performances on small maps. We would like to have both implementation to compare the results in two scenarios: pure visual odometry, conditional independent submapping.
Material
- A framework for multisensor SLAM using the world centric approach
- Papers and report about robocentric slam
Expected outcome:
- a fully functional robocentric version of the MoonSLAM framework
Required skills or skills to be acquired:
- Basic background in computer vision
- Background in Kalman filtering
- C++ programming under Linux)
Scan Matching Odometry and Multisensor SLAM (Scan Matching Odometry and Multisensor SLAM Starting from some C/C++ code for laser scan alignment and the covariance information associated to the matching, we are interested in the development of a library for the matching and fusion of laser scans under the ROS (www.ros.org) environment. From this we are interested in the development of an odometric system based on laser scan matching and in a Simultaneous Localization and Mapping system integrating scan matching with visual SLAM. The result is a complete navigation system that fuses laser and visual information to build consisten maps in an EKF-based environment.
Material:
- a MS thesis which describes the scan matching algorithms
- a BS thesis which implements a prototype of the system
Expected outcome:
- a complete system that build maps integrating laser scan and visual informtion
Required skills or skills to be acquired:
- Background on Kalman filtering
- C++ programming under Linux)
Self calibration of multiple odometric sensors (Self calibration of multiple odometric sensors mounted on the same platform An odometric sensor measures the path followed by a robot in an incremental way (e.g., wheel mounted encoders, visual odometry, scan matching based odometry, etc.) . Having several odometry sensors mounted on the same platform can significantly improve the accuracy and robustness of the overall system but requires proper calibration of relative positioning and possible biases. We are interested in the development of techniques for the self calibration of a multi sensor based odometry sensor. These techniques could be inspired by classical non-linear optimization techniques used in the hand and eye problem but they could use techniques from Simultaneous Localization and Mapping. According to the setup, some information on the real position of the system may exists (i.e., external tracking system or GPS); the approach should be able to use this information as well.
Material:
- datasets with real data
- a few odometric system implementations
- C++ libraries for non linear optimization
Expected outcome:
- software for the self calibration of a set of odometry systems mounted on the same robot
Required skills or skills to be acquired:
- C++ programming under Linux)
Statistical inference for phylogenetic trees (Statistical inference for phylogenetic trees The project will focus on the study, implementation, comparison, and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees (1, 2, 3) are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference tasks concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic
variables associated to both internal nodes and leaves of the tree.
The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.
The project is thought to be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal and implementation of new algorithms, based on recent approaches to phylogenetic inference available in the literature, as in (3) and (4). In this case the thesis requires some extra effort in order to build and consolidate some background in math in oder to understand some recent literature, especially in (mathematical) statistics and, for example, in the emerging field of algebraic statistics (5).
Other possible novel applications of phylogenetic trees have been proposed in contexts different from biology, as in (6). Malware (malicious software) is software designed to infiltrate a computer without the owner's informed consent. Often malwares are related to previous programs thought evolutionary relationships, i.e., new malwares appear as small mutations of previous softwares. We are interested in the use of techniques from phylogenetic trees to create a taxonomy of real world malwares.
Picture taken from http://www.tolweb.org/tree/ and http://www.blogscienze.com
Bibliography
- Felsenstein 2003: Inferring Phylogenies
- Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
- Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)
- Evans, S.N. and Speed, T.P. (1993). Invariants of some probability models used in phylogenetic inference. Annals of Statistics 21, 355-377.
- Lior Pachter, Bernd Sturmfels 2005, Algebraic Statistics for Computational Biology.
- A. Walenstein, E-Md. Karim, A. Lakhotia, and L. Parida. Malware Phylogeny Generation Using Permutations of Code, Journal in Computer Virology, v1.1, 2005.)
Unmanned Aerial Vehicles Visual Navigation (A critical review on the state of the art in visual navigation for unmanned aerial vehicles 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)
Visual stabilization techniques for tracking with a moving camera (Visual stabilization techniques for tracking with a moving camera Target tracking in video sequences can suffer poor performances if the camera is moving (e.g, wind, hand held device, aerial tracking system). The aim of the project is to investigate the state of the art in image stabilization and registration in non static or cluttered scenes. Possible ideas to be investigated include: homography tracking or smoothing, 3D camera motion estimation, image registration and mosicing. As a by product of the work, a tool for the performance evaluation of image stabilization algorithms should be designed.
Material
- a huge corpus of literature on the topic
- datasets to test the approach upon
- C++ library for image processing and computer vision (OpenCV)
Expected outcome:
- software for the stabilization of videos from a moving camera showing moving objects in cluttered environments
- a tool for the objective evaluation of image stabilization algorithms
Required skills or skills to be acquired:
- computer vision and 3D reconstruction
- C++ programming under Linux)
