Study of Saliency Map Estimation Method using Growing Neural Gas

Abstract

In recent years, Digitalization has become a hot topic, and in particular, the introduction of autonomous mobile robots is required in the service industry for safety and practicality. Therefore, we have been studying the traversability for autonomous robots and the degree of graspability of unknown objects using Growing Neural Gas (GNG) which is a one of unsupervised learning. However, previous methods extract pixels evenly from the input data for learning, which makes it difficult to collect information on the object of interest. In this paper, we propose a method for finding salient points in an environment using saliency which is a human gazing mechanism. Concretely, we have succeeded in obtaining the salient points of an unknown environment by dividing the features into color, intensity and orientation, and then integrating the salient points for each using GNG. Finally, we discuss the effectiveness of our proposed method using 2D images, 3D images, and benchmarks.