As spatial grammars have proven successful and efficient to deliver LoD3 models, the next challenge is their extension to indoor applications, leading to LoD4 models. Therefore, a combined indoor grammar for the automatic generation of indoor models from erroneous and incomplete observation data is presented. In building interiors where inaccurate observation data is available, the grammar can be used to make the reconstruction process robust, and verify the reconstructed geometries. In unobserved building interiors, the grammar can generate hypotheses about possible indoor geometries matching the style of the rest of the building. The grammar combines concepts from L-systems and split grammars. It is designed in such way that it can be derived from observation data fully automatically. Thus, manual predefinitions of the grammar rules usually required to tune the grammar to a specific building style, become obsolete. The potential benefit of using our grammar as support for indoor modeling is evaluated based on an example where the grammar has been applied to automatically generate an indoor model from erroneous and incomplete traces gathered by foot-mounted MEMS/IMU positioning systems.