Ricostruzione 3d da immagini rgb-d su piattaforma mobile

Viene proposto un porting su piattaforma mobile Android di un sistema SLAM (Simultaneous Localization And Mapping) chiamato SlamDunk. Il porting affronta problematiche di prestazioni e qualità delle ricostruzioni 3D ottenute, proponendo poi la soluzione ritenuta ottimale.

Sperimentazione con algoritmi per l'analisi di nuvole di punti per applicazioni di guida autonoma

Al giorno d’oggi quasi tutte le persone possiedono un mezzo motorizzato che utilizzano per spostarsi. Tale operazione, che risulta semplice per una persona, può essere compiuta da un robot o un autoveicolo in modo autonomo? La risposta a questa domanda è si, ma se ad una persona serve solo un po’ di pratica per guidare, questa azione non risulta altrettanto immediata per dei veicoli motorizzati. In soccorso ad essi vi è la Computer Vision, un ramo dell’informatica che, in un certo senso, rende un elaboratore elettronico in grado di percepire l’ambiente circostante, nel modo in cui una persona fa con i propri occhi. Oggi ci concentreremo su due campi della computer vision, lo SLAM o Simultaneous Localization and Mapping, che rende un robot in grado di mappare, attraverso una camera, il mondo in cui si trova ed allo stesso tempo di localizzare, istante per istante, la propria posizione all’interno di esso, e la Plane Detection, che permette di estrapolare i piani presenti all’interno di una data immagine.

Emotional contagion in consecutive interpreting: An empirical study with novice interpreters

It has recently been noticed that interpreters tend to converge with their speakers’ emotions under a process known as emotional contagion. Emotional contagion still represents an underinvestigated aspect of interpreting and the few studies on this topic have tended to focus more on simultaneous interpreting rather than consecutive interpreting. Korpal & Jasielska (2019) compared the emotional effects of one emotional and one neutral text on interpreters in simultaneous interpreting and found that interpreters tended to converge emotionally with the speaker more when interpreting the emotional text. This exploratory study follows their procedures to study the emotional contagion potentially caused by two texts among interpreters in consecutive interpreting: one emotionally neutral text and one negatively-valenced text, this last containing 44 negative words as triggers. Several measures were triangulated to determine whether the triggers in the negatively-valenced text could prompt a stronger emotional contagion in the consecutive interpreting of that text as compared to the consecutive interpreting of the emotionally neutral text, which contained no triggers—namely, the quality of the interpreters’ delivery; their heart rate variability values as collected with EMPATICA E4 wristbands; the analysis of their acoustic variations (i.e., disfluencies and rhetorical strategies); their linguistic and emotional management of the triggers; and their answers to the Italian version of the Positive and Negative Affect Schedule (PANAS) self-report questionnaire. Results showed no statistically significant evidence of an emotional contagion evoked by the triggers in the consecutive interpreting of the negative text as opposed to the consecutive interpreting of the neutral text. On the contrary, interpreters seemed to be more at ease while interpreting the negative text. This surprising result, together with other results of this project, suggests venues for further research.

Image Processing Algorithms applied to Radio SLAM using MIMO Radars

Radio Simultaneous Location and Mapping (SLAM) consists of the simultaneous tracking of the target and estimation of the surrounding environment, to build a map and estimate the target movements within it. It is an increasingly exploited technique for automotive applications, in order to improve the localization of obstacles and the target relative movement with respect to them, for emergency situations, for example when it is necessary to explore (with a drone or a robot) environments with a limited visibility, or for personal radar applications, thanks to its versatility and cheapness. Until today, these systems were based on light detection and ranging (lidar) or visual cameras, high-accuracy and expensive approaches that are limited to specific environments and weather conditions. Instead, in case of smoke, fog or simply darkness, radar-based systems can operate exactly in the same way. In this thesis activity, the Fourier-Mellin algorithm is analyzed and implemented, to verify the applicability to Radio SLAM, in which the radar frames can be treated as images and the radar motion between consecutive frames can be covered with registration. Furthermore, a simplified version of that algorithm is proposed, in order to solve the problems of the Fourier-Mellin algorithm when working with real radar images and improve the performance. The INRAS RBK2, a MIMO 2x16 mmWave radar, is used for experimental acquisitions, consisting of multiple tests performed in Lab-E of the Cesena Campus, University of Bologna. The different performances of Fourier-Mellin and its simplified version are compared also with the MatchScan algorithm, a classic algorithm for SLAM systems.