Scalable learning for expanding robot vision - Porav, H (2020)
Vision is hard, but the rewards for cracking the problems that stand between us and robust robot vision more than make up for the arduous journey. This work aims to guide the reader through some ideas and concepts that try to make robot vision better under difficult conditions. We tackle the shortcomings of robot vision from three different directions: domain adaptation, image de-noising and finally training data generation. However, the boundaries between these categories are fluid, allowing for complementary operation.