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The hypothesis that an atypical hemispheric specialization is associated to developmental dyslexia (DD) is receiving renewed interest, lending some support to Orton’s theory. In this article, we investigated whether interhemispheric transfer processes (IHT) are likely to be involved in developmental dyslexia. In this study, we tested 13 children with developmental dyslexia and 13 matched controls (aged 8 to 13 years) in four different tasks. In a tactile transfer task, the dyslexic children’s performance was less accurate. In a standard Poffenberger paradigm, dyslexic children performed slower than the controls in all conditions and did not show any difference between crossed and uncrossed conditions. Furthermore, they showed an increased asymmetry of performance according to the responding hand, while controls gave more coherent responses. In a visual task of object orientation discrimination, dyslexic children had slower Response Times (RTs) than controls, especially for mirror-reversed objects in the right visual field. Finally, a higher number of dyslexic children showed mirror-drawing or mirror-writing with respect to controls. Our results as a whole show that children with DD are impaired in interhemispheric transfer, although the differences in performance among dyslexic individuals suggest the impairment of different psychophysiological mechanisms. As such, a common origin in terms of connectivity problems is proposed.

Simultaneous Localization and Mapping (SLAM) has received quite a lot of attention in the last decades because of its relevance for many applications centered on a mobile observer, such as service robotics and intelligent transportation systems. This paper focuses on the use of recursive Bayesian filtering, as implemented by the Extendend Kalman Filter (EKF), to face the Visual SLAM problem, i.e., when using data from visual sources. In Monocular SLAM, which uses a single camera as unique source of information, it is not possible to directly estimate the depth of a feature from a single image. To handle the severely non-normal distribution representing such uncertainty, inverse parametrizations were developed, capable to deal with such uncertainty and still relying on Gaussian variables. In the paper, after an introduction to EKF-SLAM, we provide a review of different inverse parametrizations, and we introduce a novel proposal, the Framed Inverse Depth (FID) parametrization, which, in terms of consistency, performs similarly to state of the art Monocular SLAM parametrizations, but at a reduced computational cost. All these parametrizations can be used in a stereo and multi camera setting too. An extensive analysis is presented for both Monocular and stereo SLAM, for a simulated environment widely used in the literature as well as on a widely used real dataset.

In the last years, the Monocular SLAM problem was widely studied, to allow the simultaneous reconstruction of the environment and the localization of the observer, by using a single camera. As for other SLAM problems, a frequently used feature for the representation of the world, is the 3D point. Differently from other SLAM problems, because of the perspective model of the camera, in Monocular SLAM, features cannot be completely perceived and initialized from a single measurement. To solve this issue, different parameterizations have been proposed in the literature, which try to solve also another problem in Monocular SLAM, i.e., the distortion of the Gaussian uncertainty in depth estimation that takes place because of the non- linear measurement model. In this paper, we start from recent results in consistency analysis for these parameterizations to propose a novel approach to improve EKF-based Monocular SLAM even further. Our claims are sustained by an extended validation on simulated and real data.

In this paper we propose an improvement to the Implicit Shape Model (ISM) based robust object detection system proposed by Leibe et al. Object detection with ISM allows to approach the classification and tracking in a probabilistic way with multiple hypotheses. Unlike the original approach, our method is independent from object scale in the training sets, and this allows to work with a much smaller training sets and also to avoid to supply information about scale to the trainer. This is done while maintaining the robustness of the original approach. Leibe et al. mentioned a potential solution to overcome the scale problem in the training set, i.e., the usage of the scale produced by the local descriptor. Our proposal is different: since we believe that the scale measure generated by local descriptors is subject to noise, we try to walk around this noise by estimating the scale measure from the only evidence collected in the image.

Recent works have shown that it is possible to solve the Simultaneous Localization And Mapping problem using an Extended Kalman Filter and a single perspective camera. The principal drawback of these works is an inaccurate modeling of measurement uncertainties, which therefore causes inconsistencies in the filter estimations. A possible solution to proper uncertainty modeling is the Unified Inverse Depth parametrization. In this paper we propose the Inverse Scaling parametrization that still allows an un-delayed initialization of features, while reducing the number of needed parameters and simplifying the measurement model. This novel approach allows a better uncertainty modeling of both low and high parallax features and reduces the likelihood of inconsistencies. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the performance of the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach; experiment on real data confirm the applicability of the idea

A trustable and accurate ground truth is a key requirement for benchmarking self-localization and mapping algorithms; on the other hand, collection of ground truth is a complex and daunting task, and its validation is a challenging issue. In this paper we propose two techniques for indoor ground truth collection, developed in the framework of the European project Rawseeds, which are mutually independent and also independent on the sensors onboard the robot. These techniques are based, respectively, on a network of fixed cameras, and on a network of fixed laser scanners. We show how these systems are implemented and deployed, and, most importantly, we evaluate their performance; moreover, we investigate the possible fusion of their outputs. © 2009 Springer Science+Business Media, LLC.

In this paper we face the issue of fusing 3D data from different sensors in a seamless way, using the unifying framework of uncertain projective geometry. Within this framework it is possible to describe, combine, and estimate various types of geometric elements (2D and 3D points, 2D and 3D lines, and 3D planes) taking their uncertainty into account. Because of the size of the data involved in this process, the integration process and thus the SLAM algorithm turns out to be very slow. For this reason, in this work, we propose the use of an R*-Tree data structure to speed up the whole process, managing in an efficent way both the estimated map and the 3D points clouds coming out from the stereo camera. The experimental section shows that the use of uncertain projective geometry and the R*-Tree data structure improves the mapping and the pose estimation

The recent literature has shown that it is possible to solve the monocular Simultaneous Localization And Mapping using both undelayed features ini- tialization and an Extedend Kalman Filter. The key concept, to achieve this result, was the introduction of a new parametrization called Unified Inverse Depth that produces measurements equations with a high degree of linear- ity and allows an efficient and accurate modeling of uncertainties. In this paper we present a monocular EKF SLAM filter based on an alternative parametrization, i.e., the Inverse Scaling Parametrization, characterized by a reduced number of parameters, a more linear measurement model, and a bet- ter modeling of features uncertainty for both low and high parallax features. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach, while experiments on real data show the system working in practice as well.

Self localization and mapping with vision is still an open research field. Since redundancy in the sensing suite is too expensive for consumer-level robots, we base on vision as the main sensing system for SLAM. We approach the problem with 3D data from a trinocular vision system. Past experience shows that problems arise as a consequence of inaccurate modeling of uncertainties; interestingly enough, we found that accuracy in modeling the robot pose uncertainty is much less relevant than for the uncertainty on the sensed data. To overcome the severe limitation of linear and Gaussian approximations, we applied a particle-based description of the inherently nonnormal probability density distribution of the sensed data; the aim is to increase the success rate of data association, which we see as the most important problem. The increase in correct data associations reduces the uncertainty in the model and, consequently, in the robot pose, respectively estimated with a hierarchical map decomposition and a six degree of freedom extended Kalman filter. In this paper, we present approaches for particle-based sensor modeling and data association, with a comparative experimental evaluation on real 3D vision data.

Descrizione della piattaforma robotica mobile per le attività di raccolta dati indoor del progetto EEC RAWSEEDS. Il robot effettuava raccolte dati multi-sensoriali sincronizzate tra loro e con il sistema esterno di raccolta della Ground-Truth della robot pose. L'elenco di sensori a bordo e dei dati da questi generati, da raccogliersi senza perdite, e' ancora oggi impressionante. Si sono risolti svariati problemi, dalla meccanica, alla elettronica analogica, digitale, di software di base e di alto livello, di algoritmi di percezione robotica per la calirazione ei parametri, soprattutto geometrici. La attività è stata svolta per il supporto al benchmarking degli algoritmi di SLAM per robotica mobile.