AI RESEARCH
DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
arXiv CS.CV
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ArXi:2605.02759v1 Announce Type: cross Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their applicability in real-world spaces populated by moving entities, such as pedestrians. In this work, we propose DynoSLAM, a tightly-coupled Dynamic GraphSLAM architecture that integrates socially-aware Graph Neural Networks (GNNs) directly into the factor graph optimization.