Spatial deep learning can be used to make sense of the system’s surroundings. The data in the 3D map of the space around the platform is “translated” to information that can be readily interpreted by a human. For example, an object within the map can be identified as something specific, like a table, a park bench or a road sign. Spatial deep learning plays a very important role in autonomous driving, making it possible for a vehicle to identify key landmarks within the 3D map being used for navigation.
Univrses’ semantic interpretation solution is being developed to work in a range of different areas. For instance, in automotive, we can build semantic maps of the environment so road signs, traffic lights, houses and other features can be identified. The component can also be deployed on a mobile robot to understand the location of a loading palette.