  The imagination of the surrounding environment based on experience and
semantic cognition has great potential to extend the limited observations and
provide more information for mapping, collision avoidance, and path planning.
This paper provides a training-based algorithm for mobile robots to perform
spatial imagination based on semantic cognition and evaluates the proposed
method for the mapping task. We utilize a photo-realistic simulation
environment, Habitat, for training and evaluation. The trained model is
composed of Resent-18 as encoder and Unet as the backbone. We demonstrate that
the algorithm can perform imagination for unseen parts of the object
universally, by recalling the images and experience and compare our approach
with traditional semantic mapping methods. It is found that our approach will
improve the efficiency and accuracy of semantic mapping.
