In recent years, 3D scene graphs have become a critical tool in robotics and computer vision for enabling systems to understand both the geometric and semantic aspects of their surroundings. These data structures represent spatial and semantic relationships between objects in a three-dimensional environment, supporting tasks like navigation, object manipulation, and scene understanding. This paper presents a real-time pipeline for 3D scene graph generation that offers flexibility in image segmentation techniques while incorporating room classification that is based on a Random Forest model. Our work enables robots to dynamically update their understanding of complex and large-scale environments in real-time. We evaluate our approach systematically on a dataset and in a real-life experiment. The results demonstrate the capability of running our solution at over 10 Hz on an Nvidia Jetson AGX Orin SoC while also scaling favorably in larger environments. Our proposed room classification approach predicts classes with an average accuracy of 80%.

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

Modern ubiquitous computing environments are increasingly populated with smart devices that need to know the identity of users interacting with them. At the same time, the number of authentications that a user needs to perform increases, as nowadays devices such as smart TVs require authentication which was not the case in earlier times. Even for single-person households, the need to authenticate against present smart devices in the environment appears at regular intervals, ranging from TVs to voice assistants, to gaming consoles. To reduce the need for repeated authentication, we explore the concept of a system that allows the sharing of users’ authenticated identity information between smart devices, similar to the concept of Single-Sign-On on the internet. Following a preliminary field study, we show that such a system can decrease the number of necessary authentications in a ubiquitous computing environment by 84.4%, increasing usability and security.