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| PrjDescription | PrjResArea | PrjTutor | PrjStudMin | PrjStudMax | |
|---|---|---|---|---|---|
| Interpretation of facial expressions and movements of the head | Affective Computing | MatteoMatteucci | |||
| LARS and LASSO in non Euclidean Spaces | LASSO (1) and more recently LARS (2) are two algorithms proposed for linear regression tasks. In particular LASSO solves a least-squares (quadratic) optimization problem with a constrain that limits the sum of the absolute value of the coefficients of the regression, while LARS can be considered as a generalization of LASSO, that provides a more computational efficient way to obtain the solution of the regression problem simultaneously for all values of the constraint introduced by LASSO. One of the common hypothesis in regression analysis is that the noise introduced in order to model the linear relationship between regressors and dependent variable has a Gaussian distribution. A generalization of this hypothesis leads to a more general framework, where the geometry of the regression task is no more Euclidean. In this context different estimation criteria, such as maximum likelihood estimation and other canonical divergence functions do not coincide anymore. The target of the project is to compare the different solutions associated to different criteria, for example in terms of robustness, and propose generalization of LASSO and LARS in non Euclidean contexts. 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 project has also a theoretical flavor, mathematical inclined students are encouraged to apply. The project may require some extra effort in order to build and consolidate some background in math, especially in probability and statistics. Picture taken from (2) Bibliography # Tibshirani, R. (1996), Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc B., Vol. 58, No. 1, pages 267-288 # Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, Least Angle Regression, 2003 | Machine Learning | MatteoMatteucci LuigiMalago |
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| 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. | Robotics | MartinoMigliavacca MatteoMatteucci |
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| Machine Learning for Crop Weed Classification | Automatic detection of crops and weeds by using image data | Machine Learning | MatteoMatteucci | ||
| MoonSLAM Reengineering | 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 | Robotics | MatteoMatteucci | 1 | 2 |
| Multimodal GUI for driving an autonomous wheelchair | This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair (LURCH - The autonomous wheelchair) with a multi modal interface (Speech Recognition, Brain-Computer Interface, etc.), through the development of key software modules. The work will be validated with live experiments. *R. Blatt et al. ''Brain Control of a Smart Wheelchair'' (http://tinyurl.com/ygldwun) | BioSignal Analysis | MatteoMatteucci SimoneCeriani DavideMigliore |
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| Object Recognition with Deep Boltzmann Machines | Deep Boltzmann machines for classification tasks | Robotics | MatteoMatteucci FrancescoVisin |
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| Odometric system for robots based on laser mice | We developed an odometric system for robots by combining the reading of several laser mice. The system consists of a master PIC-based board and several slave boards where the sensors employed in optical mice are located. The readings are collected on the PIC and sent on the serial port to a PC which elaborates and combines the x and y readings in order to obtain a x,y,theta estimation of the movement of the robot. The aim of the project is first to improve the current design of the PIC-based board, and realize a new working prototype, and then to implement and evaluate different algorithms able to estimate more precisely the x,y and theta odometric data from the mice readings. Experience with PIC-based systems and some experience with electronics circuits is a plus. Students are supposed to redesign the electronic board, improve the firmware of the PIC, and work on the algorithm that estimates the robot position on the PC. It would be also interesting to evaluate the possibility to embed the optimization and estimation algorithms in the firmware of the PIC in order to produce a stand-alone device. Ask the tutors of the project for extra material, such as data-sheets and other documentation. | Robotics | MatteoMatteucci LuigiMalago MarcelloRestelli |
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| P300 BCI | Recovery, integration and adaptation of P300 BCI (hardware and software) stubs to generate a working interface for a speller. The aim is to develop a working prototype for an ALS affected patient. | BioSignal Analysis | MatteoMatteucci | 1 | 3 |
| Poit cloud SLAM with Microsoft Kinect | 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 | Computer Vision and Image Analysis | MatteoMatteucci | 1 | 2 |
| R2P | The goal of the project is to develop a HW/SW system for rapid prototyping of robots | Robotics | AndreaBonarini MatteoMatteucci |
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| R2P IMU firmware development | 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 | Robotics | AndreaBonarini MartinoMigliavacca MatteoMatteucci |
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| 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. * J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' (http://tinyurl.com/yhq27pq) * R.J. Croff, R.J. Barry. ''Removal of ocular artifact from the EEG: a review'' (http://tinyurl.com/ykk6ks9) | BioSignal Analysis | MatteoMatteucci | 1 | 2 |
| Recognition of the user's focusing on the stimulation matrix | A P300-based Brain-Computer Interface (BCI) stimulates the user continuously, and the detection of a P300 designates the choice of the user. When the user is not paying attention to the interface, false positives are likely. The objective of this work is to avoid this problem; the analysis of the electroencephalogram (EEG) over the visual cortex (and possibly an analysis of P300s or of other biosignals) should tell when the user is looking at the interface | BioSignal Analysis | MatteoMatteucci | ||
| RoboCom+R2P | The goal of the project is to develop a Robot equipped with collision avoidance logic. | MatteoMatteucci | |||
| Robocentric MoonSLAM | 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 | Robotics | MatteoMatteucci SimoneCeriani |
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| Robot Localization and Navigation With Visual Markers | Robot Localization and Navigation With Visual Markers | Robotics | MatteoMatteucci AndreaBonarini |
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| SLDR | Computer Vision and Image Analysis | MatteoMatteucci | |||
| 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 | Robotics | MatteoMatteucci SimoneCeriani DavideCucci |
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| Self calibration of multiple odometric sensors | 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 | Robotics | MatteoMatteucci SimoneCeriani DavideCucci |
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