Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Controle de aeromodelo empregando visão computacional

Controle e navegação são tarefas bastante difundidas no campo da robótica, desempenhando atividades como o auxílio em missões de risco e navegação em terreno desconhecido. Este artigo apresenta uma metodologia para o controle e navegação assistida de aeromodelos, empregando técnicas de visão computacional e reconhecimento de padrões. Os experimentos realizados demonstram a capacidade da metodologia para aplicações de tempo real