  The ability to endow maps of indoor scenes with semantic information is an
integral part of robotic agents which perform different tasks such as target
driven navigation, object search or object rearrangement. The state-of-the-art
methods use Deep Convolutional Neural Networks (DCNNs) for predicting semantic
segmentation of an image as useful representation for these tasks. The accuracy
of semantic segmentation depends on the availability and the amount of labeled
data from the target environment or the ability to bridge the domain gap
between test and training environment. We propose RegConsist, a method for
self-supervised pre-training of a semantic segmentation model, exploiting the
ability of the agent to move and register multiple views in the novel
environment. Given the spatial and temporal consistency cues used for pixel
level data association, we use a variant of contrastive learning to train a
DCNN model for predicting semantic segmentation from RGB views in the target
environment. The proposed method outperforms models pre-trained on ImageNet and
achieves competitive performance when using models that are trained for exactly
the same task but on a different dataset. We also perform various ablation
studies to analyze and demonstrate the efficacy of our proposed method.
