Semantic Mapping

Semantic mapping links observations or data fields to meaningful concepts, categories, entities, and relations so machines can reason beyond raw measurements. In robotics, systems combine SLAM and RGB-D geometry with object instances, scene graphs, voxel maps, and open-vocabulary features from CLIP or vision-language models; persistent fusion and active exploration support querying and navigation. For structured data, ontology and schema matching uses language-model embeddings, graph and instance evidence, constraints, retrieval, and human review to align concepts across sources. Current work is moving from static labels toward open-set, temporal, uncertainty-aware maps, while reliability under viewpoint or domain shifts, changing scenes, memory limits, and ambiguous mappings remains a central challenge.