Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where environmental conditions may change significantly over time. Although recent advances in Visual Foundation Models (VFMs) have improved open-vocabulary semantic segmentation, t...
Michele Antonazzi, A. C. Hernández, José Araújo et al.· 0 citations
3D scene graphs provide semantically rich and hierarchical representations for robot perception. However, existing systems do not maintain uncertainty as an explicit belief or propagate it through the operations that construct and refine the graph. We introduce Probabilistic Scene Graph (PSG), a generalization of the c...
Waqas Ali, Michele Antonazzi, Timon Homberger et al.· 0 citations
This work addresses safe control under the induced multi-object state uncertainty with a risk-aware belief control barrier function (BCBF) framework and establishes forward invariance of the safe set under continuous prediction, and derives an explicit condition under which discrete updates preserve safety.
Shaohang Han, Gang Chen, Yi-Xi Cai et al.· arXiv.org· 0 citations
Robots operating in human environments need memories that capture not only what objects exist and where, but also how people use them over time and how individual interactions compose into goal-directed activities. Existing 4D scene graphs preserve object and place histories but omit activity structure, whereas activit...
Ermanno Bartoli, Buwei He, Dennis Rotondi et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
