Robots for Exploration, Digital Preservation and Visualization of Archeological Sites

Monitoring and conservation of archaeological sites are important activities necessary to prevent damage or to perform restoration on cultural heritage. Standard techniques, like mapping and digitizing, are typically used to document the status of such sites. While these task are normally accomplished manually by humans, this is not possible when dealing with hard-to-access areas. For example, due to the possibility of structural collapses, underground tunnels like catacombs are considered highly unstable environments. Moreover, they are full of radioactive gas radon that limits the presence of people only for few minutes. The progress recently made in the artificial intelligence and robotics field opened new possibilities for mobile robots to be used in locations where humans are not allowed to enter. The ROVINA project aims at developing autonomous mobile robots to make faster, cheaper and safer the monitoring of archaeological sites. ROVINA will be evaluated on the catacombs of Priscilla (in Rome) and S. Gennaro (in Naples)

3-D Map Merging on Pose Graphs

In this letter, we propose an approach for merging three-dimensional maps represented as pose graphs of point clouds. Our method can effectively deal with typical distortions affecting simultaneous localization and mapping-generated maps. Traditional map merging techniques that use a single rigid body transformation to relate the reference frames of different maps. Instead, our approach achieves more accurate results by eliminating the inconsistencies resulting from distortions affecting the inputs, and can succeed in those situations where traditional approaches fail for substantial deformations. The core idea behind our solution is to localize the robot in a reference map by using the data from another map as observations. We validated our approach on publicly available datasets, and provide quantitative results that confirm its effectiveness on challenging instances of the merging problem

Knowledge acquisition through human-robot multimodal interaction

The limited understanding of the surrounding environment still restricts the capabilities of robotic systems in real world applications. Specifically, the acquisition of knowledge about the environment typically relies only on perception, which requires intensive ad hoc training and is not sufficiently reliable in a general setting. In this paper, we aim at integrating new acquisition devices, such as tangible user interfaces, speech technologies and vision-based systems, with established AI methodologies, to present a novel and effective knowledge acquisition approach. A natural interaction paradigm is presented, where humans move within the environment with the robot and easily acquire information by selecting relevant spots, objects, or other relevant landmarks. The synergy between novel interaction technologies and semantic knowledge leverages humans' cognitive skills to support robots in acquiring and grounding knowledge about the environment; such richer representation can be exploited in the realization of robot autonomous skills for task accomplishment. © 2012 Springer-Verlag Berlin Heidelberg