Building of cognizing semantic map in large-scale semi-unknown environment

Abstract

The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only with robot’s vision. By imitating spatial cognizing mechanism of human, the robot constantly received the information of artificial labels at cognitive-guide points in a wide range of structured environment to achieve the perception of the environment and robot navigation. The immune network algorithm was used to form the environmental awareness mechanism with “distributed representation”. The color recognition and SIFT feature matching algorithm were fused to achieve the memory and cognition of scenario tag. Then the cognition-guide-action based cognizing semantic map was built. Along with the continuously abundant map, the robot did no longer need to rely on the artificial label, and it could plan path and navigate freely. Experimental results show that the artificial label designed in this work can improve the cognitive ability of the robot, navigate the robot in the case of semi-unknown environment, and build the cognizing semantic map favorably.