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.
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} }