POV-SLAM：半静态环境中的概率对象感知变分SLAM
POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments
AI总结：
提出一种对象感知的因子图SLAM框架，通过变分期望最大化策略优化高斯-均匀双峰似然，跟踪半静态物体变化，提升长期定位鲁棒性。
摘要：
在缓慢变化的场景中进行同步定位与建图（SLAM）对于长期机器人任务完成至关重要。未能检测到场景变化可能导致地图不准确，最终导致机器人迷失。经典SLAM算法假设场景是静态的，而最近的研究考虑了动态性，但要求场景变化在连续帧中被观察到。半静态场景，即物体随时间出现、消失或缓慢移动，常被忽视，但对长期操作至关重要。我们提出了一种对象感知的因子图SLAM框架，用于跟踪和重建半静态对象级变化。我们采用新颖的变分期望最大化策略，优化涉及高斯-均匀双峰测量似然的因子图，以处理潜在变化的物体。我们在模拟环境及我们新颖的真实世界SLAM数据集上评估了该方法，该数据集是在一个仓库中历时四个月采集的。我们的方法在存在半静态变化时提高了定位的鲁棒性，提供了场景的对象级推理。
英文摘要：
Simultaneous localization and mapping (SLAM) in slowly varying scenes is important for long-term robot task completion. Failing to detect scene changes may lead to inaccurate maps and, ultimately, lost robots. Classical SLAM algorithms assume static scenes, and recent works take dynamics into account, but require scene changes to be observed in consecutive frames. Semi-static scenes, wherein objects appear, disappear, or move slowly over time, are often overlooked, yet are critical for long-term operation. We propose an object-aware, factor-graph SLAM framework that tracks and reconstructs semi-static object-level changes. Our novel variational expectation-maximization strategy is used to optimize factor graphs involving a Gaussian-Uniform bimodal measurement likelihood for potentially-changing objects. We evaluate our approach alongside the state-of-the-art SLAM solutions in simulation and on our novel real-world SLAM dataset captured in a warehouse over four months. Our method improves the robustness of localization in the presence of semi-static changes, providing object-level reasoning about the scene.
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