The Warehouse Map Learned When the Same Box Was Different

A long-running robot can return to a shelf whose geometry looks unchanged even though one object has been replaced by a similar instance. FOCUS treats that ambiguity as probabilistic inference, combining geometric evidence with appearance rendered from a 3D Gaussian map and maintaining three states for each object: persisted, replaced or removed. On an Isaac Sim warehouse benchmark built around ambiguous substitutions, the paper reports replacement F1 rising from 0.20 to 0.84 over a probabilistic baseline. It also reports transfer to the real-world TorWIC data without manual parameter retuning. The work is less about seeing change once than maintaining a memory that knows what kind of change it saw.