Semantic segmentation networks are usually pre-trained once and not updated during
deployment. As a consequence, misclassifications commonly occur if the distribution of the
training data deviates from the one encountered during the robot's operation. We propose to
mitigate this problem by adapting the neural network to the robot's environment during
deployment, without any need for external supervision. Leveraging complementary data
representations, we generate a supervision signal, by probabilistically accumulating …