Our goal for this project is to set up the basis for a new generation of smart autonomous agents that are able to carry out 3D perception in real-time, using biologically-inspired vision sensors. These sensors independently processed all pixels and only trigger changes in the scene (events) in the case a substantial difference in the intensity luminance happens over time for a specific location (this happens only at object contours and textures). This allows for the reduction of the transmission of redundant information and hence, the data bandwidth.
BRAINAV builds on the integration of visual processing pipelines for energy-efficient edge processing using neuromorphic strategies, for the application of autonomous navigation. First, visual processing is crucial in perception system components and specifically, for scene awareness in navigation applications. Robotic agents require understanding their context to plan and make decisions accordingly. This is even more relevant in applications such as robotics. However, computer vision is a very demanding task in terms of resources, and effective navigation requires low latencies to close perception-action loops in real-time. Second, regarding 3D perception and scene understanding, state-of-the-art solutions are focused on accuracy performance but other qualities such as energy consumption must be also taken into account.