Visual SLAM using straight lines

The present thesis is focuses on the problem of Simultaneous Localisation and Mapping (SLAM) using only visual data (VSLAM). This means to concurrently estimate the position of a moving camera and to create a consistent map of the environment. Since implementing a whole VSLAM system is out of the scope of a degree thesis, the main aim is to improve an existing visual SLAM system by complementing the commonly used point features with straight line primitives. This enables more accurate localization in environments with few feature points, like corridors. As a foundation for the project, ScaViSLAM by Strasdat et al. is used, which is a state-of-the-art real-time visual SLAM framework. Since it currently only supports Stereo and RGB-D systems, implementing a Monocular approach will be researched as well as an integration of it as a ROS package in order to deploy it on a mobile robot. For the experimental results, the Care-O-bot service robot developed by Fraunhofer IPA will be used.

Detección de personas basada en HOG y SVM

El presente proyecto se centra en proteger a los peatones de las vías urbanas o de una fábrica donde conviven con robots móviles, pues son los mayores afectados en los acci- dentes producidos en estos entornos. El objetivo es diseñar un algoritmo basado en visión monocular capaz de detectar a los usuarios de forma rápida y precisa de tal forma que se tenga constancia en todo momento de los peatones que se encuentran delante del vehículo.