Robotics: Science and Systems XVIII

POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes

Jingxing Qian^*, Veronica Chatrath^*, Jun Yang, James Servos, Angela P. Schoellig, Steven L. Waslander
^* These authors contributed equally

Abstract:

Maintaining an up-to-date map to reflect recent changes in the scene is very important, particularly in situations involving repeated traversals by a robot operating in an environment over an extended period. Undetected changes may cause a deterioration in map quality, leading to poor localization, inefficient operations, and lost robots. Volumetric methods, such as truncated signed distance functions (TSDFs), have quickly gained traction due to their real-time production of a dense and detailed map, though map updating in scenes that change over time remains a challenge. We propose a framework that introduces a novel probabilistic object state representation to track object pose changes in semi-static scenes. The representation jointly models a stationarity score and a TSDF change measure for each object. A Bayesian update rule that incorporates both geometric and semantic information is derived to achieve consistent online map maintenance. To extensively evaluate our approach alongside the state-of-the-art, we release a novel real-world dataset in a warehouse environment. We also evaluate on the public ToyCar dataset. Our method outperforms state-of-the-art methods on the reconstruction quality of semi-static environments.

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Bibtex:

  
@INPROCEEDINGS{Qian-RSS-22, 
    AUTHOR    = {Jingxing Qian AND Veronica Chatrath AND Jun Yang AND James Servos AND {Angela P.} Schoellig AND {Steven L.} Waslander}, 
    TITLE     = {{POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2022}, 
    ADDRESS   = {New York City, NY, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2022.XVIII.013} 
}