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.

翻译：摘要：缓慢变化场景中的同步定位与地图构建对于机器人长期任务执行至关重要。未能检测到场景变化可能导致地图不准确，最终造成机器人定位丢失。经典SLAM算法假设静态场景，近年研究虽考虑动态因素，但要求场景变化在连续帧中可观测。半静态场景中物体随时间缓慢出现、消失或移动，这类场景常被忽视，却是长期运行的关键。我们提出一种基于对象感知的因子图SLAM框架，可跟踪并重建半静态对象级变化。通过新颖的变分期望最大化策略，我们对潜在变化对象采用高斯-均匀双模态测量似然性优化因子图。在仿真环境及我们捕获自真实仓库四个月观测期的新型SLAM数据集上，将本方法与先进SLAM方案进行对比评估。该方法在存在半静态变化时提升了定位鲁棒性，并实现了对场景的对象级推理。