Abordagem dirigida a modelos para redes de sensores sem fio

Research on Wireless Sensor Networks (WSN) has evolved, with potential applications in several domains. However, the building of WSN applications is hampered by the need of programming in low-level abstractions provided by sensor OS and of specific knowledge about each application domain and each sensor platform. We propose a MDA approach do develop WSN applications. This approach allows domain experts to directly contribute in the developing of applications without needing low level knowledge on WSN platforms and, at the same time, it allows network experts to program WSN nodes to met application requirements without specific knowledge on the application domain. Our approach also promotes the reuse of the developed software artifacts, allowing an application model to be reused across different sensor platforms and a platform model to be reused for different applications

Odometria visual baseada em técnicas de structure from motion

Visual Odometry is the process that estimates camera position and orientation based solely on images and in features (projections of visual landmarks present in the scene) extraced from them. With the increasing advance of Computer Vision algorithms and computer processing power, the subarea known as Structure from Motion (SFM) started to supply mathematical tools composing localization systems for robotics and Augmented Reality applications, in contrast with its initial purpose of being used in inherently offline solutions aiming 3D reconstruction and image based modelling. In that way, this work proposes a pipeline to obtain relative position featuring a previously calibrated camera as positional sensor and based entirely on models and algorithms from SFM. Techniques usually applied in camera localization systems such as Kalman filters and particle filters are not used, making unnecessary additional information like probabilistic models for camera state transition. Experiments assessing both 3D reconstruction quality and camera position estimated by the system were performed, in which image sequences captured in reallistic scenarios were processed and compared to localization data gathered from a mobile robotic platform