Robotics: Science and Systems XIX

POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments

Jingxing Qian, Veronica Chatrath, James Servos, Aaron Mavrinac, Wolfram Burgard, Steven L Waslander, Angela Schoellig


Simultaneous localization and mapping (SLAM) in slowly varying scenes is important for long-term robot task completion in GPS-denied environments. 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.



    AUTHOR    = {Jingxing Qian AND Veronica Chatrath AND James Servos AND Aaron Mavrinac AND Wolfram Burgard AND Steven L Waslander AND Angela Schoellig}, 
    TITLE     = {{POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2023}, 
    ADDRESS   = {Daegu, Republic of Korea}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2023.XIX.069}