DynoSLAM：基于生成图神经网络的动态SLAM用于现实世界的社交导航
DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
AI总结：
本文提出DynoSLAM，通过整合社交感知图神经网络，解决动态环境中SLAM的不确定性问题，提升机器人在拥挤环境中的导航能力。
摘要：
传统同时定位与建图（SLAM）算法依赖于静态环境假设，限制了其在现实世界中的应用，尤其是存在移动实体（如行人）的场景。本文提出DynoSLAM，一种紧密耦合的动态图SLAM架构，将社交感知的图神经网络（GNN）直接整合到因子图优化中。与传统方法使用刚性常速启发式或确定性单体神经先验不同，我们的框架将行人运动预测建模为随机世界模型。通过利用训练好的GNN的蒙特卡洛回放，我们捕捉人类互动的多模态认知不确定性，并通过动态马氏距离因子将其嵌入SLAM图中。通过广泛的模拟实验，我们证明这种随机建模不仅保持了高度准确的回顾跟踪，还能防止由确定性
英文摘要：
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. Unlike conventional approaches that use rigid constant-velocity heuristics or deterministic single-agent neural priors, our framework formulates pedestrian motion forecasting as a stochastic World Model. By utilizing Monte Carlo rollouts from a trained GNN, we capture the multimodal epistemic uncertainty of human interactions and embed it into the SLAM graph via a dynamic Mahalanobis distance factor. We demonstrate through extensive simulated experiments that this stochastic formulation not only maintains highly accurate retrospective tracking but also prevents the optimization failures caused by the deterministic "argmax problem". Ultimately, extracting the empirical mean and covariance matrices of future pedestrian states provides a mathematically rigorous, probabilistic safety envelope for downstream local planners, enabling anticipatory and collision-free robot navigation in densely crowded environments.
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