Ricerc@Sapienza

Semantic mapping is fundamental to enable cognition and high-level planning in robotics. It is a difficult task due to generalization to different scenarios and sensory data types. Hence, most techniques do not obtain a rich and accurate semantic map of the environment and of the objects therein. To tackle this issue we present a novel approach that exploits active vision and drives environment exploration aiming at improving the quality of the semantic map.

Nowadays, underwater acoustic communication is an interesting topic of research. This is especially true in the case of Autonomous Underwater Vehicles (AUVs): due to the limitations of the underwater environment, where WiFi and radio communications are limited, it is very important to rely on efficient and reliable tools for acoustic data exchange. Indeed, an underwater network of acoustic nodes can enhance the navigation and localization capabilities of one or more vehicles.

In this paper, we describe a navigation system requiring very few computational resources, but still providing performance comparable with commonly used tools in the ROS universe. This lightweight navigation system is thus suitable for robots with low computational resources and provides interfaces for both ROS and NAOqi middlewares.