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Identifying the environment's structure, i.e., to detect core components asrooms and walls, can facilitate several tasks fundamental for the successfuloperation of indoor autonomous mobile robots, including semantic environmentunderstanding. These robots often rely on 2D occupancy maps for core tasks suchas localisation and motion and task planning. However, reliable identificationof structure and room segmentation from 2D occupancy maps is still an openproblem due to clutter (e.g., furniture and movable object), occlusions, andpartial coverage. We propose a method for the RObust StructurE identificationand ROom SEgmentation (ROSE^2 ) of 2D occupancy maps, which may be clutteredand incomplete. ROSE^2 identifies the main directions of walls and is resilientto clutter and partial observations, allowing to extract a clean, abstractgeometrical floor-plan-like description of the environment, which is used tosegment, i.e., to identify rooms in, the original occupancy grid map. ROSE^2 istested in several real-world publicly-available cluttered maps obtained indifferent conditions. The results show how it can robustly identify theenvironment structure in 2D occupancy maps suffering from clutter and partialobservations, while significantly improving room segmentation accuracy. Thanksto the combination of clutter removal and robust room segmentation ROSE^2consistently achieves higher performance than the state-of-the-art methods,against which it is compared.

Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps