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

Jingxing Qian
University of Toronto
Veronica Chatrath
Technical University Munich
James Servos
Clearpath Robotics
Aaron Mavrinac
Clearpath Robotics
Wolfram Burgard
University of Technology, Nuremberg
Steven L Waslander
University of Toronto
Angela Schoellig
Technical University Munich
Paper Website

Paper ID 69

Session 9. Robot State Estimation

Poster Session Thursday, July 13

Poster 5

Abstract: 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.