Recent developments in autonomous driving have led to very effective vehicles that can coexist with legacy, connected and other autonomous cars while obeying the road rules. One of the main challenges for autonomous cars is to reach coexistence with pedestrians and cyclists in a safe but efficient manner, which implies being able to foresee the street users intentions/future motions in the short and mid term.
In this project, we design algorithms to control quadcopters formations in a distributed way, using consensus theory. One of the main difficulties is that the trajectories of the quadcopters should be without collision, with either static obstacles in the environment or with other quadcopters.
In this project, we show how visual constraints such as homographies and fundamental matrices can be integrated tightly into the locomotion controller of a humanoid robot to drive it from one configuration to another, only by means of images
Several online real-world stereo datasets exist for the development and testing of algorithms in the fields of perception and navigation of autonomous vehicles. However, none of them was recorded in developing countries, and therefore they lack the particular challenges that can be found on their streets and roads, like abundant potholes, irregular speed bumpers, and peculiar flows of pedestrians. We introduce a novel dataset that possesses such characteristics.