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We present a new approach for room categorization that is based on two-dimensional laser range data. The method is based on a novel spatial model consisting of mid-level parts that are built on top of a low-level part-based representation. The approach is then fused with a vision-based method for room categorization, which is also based on a spatial model consisting of mid-level visual parts. In addition, we propose a new discriminative dictionary learning technique that is applied for part-dictionary selection in both laser-based and vision-based modalities. 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