  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.
