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In order to operate in human environments, a robot's semantic perception hasto overcome open-world challenges such as novel objects and domain gaps.Autonomous deployment to such environments therefore requires robots to updatetheir knowledge and learn without supervision. We investigate how a robot canautonomously discover novel semantic classes and improve accuracy on knownclasses when exploring an unknown environment. To this end, we develop ageneral framework for mapping and clustering that we then use to generate aself-supervised learning signal to update a semantic segmentation model. Inparticular, we show how clustering parameters can be optimized duringdeployment and that fusion of multiple observation modalities improves novelobject discovery compared to prior work. Models, data, and implementations canbe found at https://github.com/hermannsblum/scim

SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding