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Scene understanding is an important capability for robots acting inunstructured environments. While most SLAM approaches provide a geometricalrepresentation of the scene, a semantic map is necessary for more complexinteractions with the surroundings. Current methods treat the semantic map aspart of the geometry which limits scalability and accuracy. We propose torepresent the semantic map as a geometrical mesh and a semantic texture coupledat independent resolution. The key idea is that in many environments thegeometry can be greatly simplified without loosing fidelity, while semanticinformation can be stored at a higher resolution, independent of the mesh. Weconstruct a mesh from depth sensors to represent the scene geometry and fuseinformation into the semantic texture from segmentations of individual RGBviews of the scene. Making the semantics persistent in a global mesh enables usto enforce temporal and spatial consistency of the individual view predictions.For this, we propose an efficient method of establishing consensus betweenindividual segmentations by iteratively retraining semantic segmentation withthe information stored within the map and using the retrained segmentation tore-fuse the semantics. We demonstrate the accuracy and scalability of ourapproach by reconstructing semantic maps of scenes from NYUv2 and a scenespanning large buildings.

Semi-Supervised Semantic Mapping through Label Propagation with Semantic Texture Meshes