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Maintaining an up-to-date map to reflect recent changes in the scene is veryimportant, particularly in situations involving repeated traversals by a robotoperating in an environment over an extended period. Undetected changes maycause a deterioration in map quality, leading to poor localization, inefficientoperations, and lost robots. Volumetric methods, such as truncated signeddistance functions (TSDFs), have quickly gained traction due to their real-timeproduction of a dense and detailed map, though map updating in scenes thatchange over time remains a challenge. We propose a framework that introduces anovel probabilistic object state representation to track object pose changes insemi-static scenes. The representation jointly models a stationarity score anda TSDF change measure for each object. A Bayesian update rule that incorporatesboth geometric and semantic information is derived to achieve consistent onlinemap maintenance. To extensively evaluate our approach alongside thestate-of-the-art, we release a novel real-world dataset in a warehouseenvironment. We also evaluate on the public ToyCar dataset. Our methodoutperforms state-of-the-art methods on the reconstruction quality ofsemi-static environments.

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