Improved Interval Type-2 Fuzzy Subtractive Clustering for obstacle detection of robot vision from stream of Depth Camera

Abstract

Obstacle detection is a fundamental issue of robot navigation and there have been several proposed methods for this problem. In this paper, we propose a new approach to find out obstacles on Depth Camera streams. The proposed approach consists of three stages. First, preprocessing stage is for noise removal. Second, different depths in a frame are clustered based on the Interval Type-2 Fuzzy Subtractive Clustering algorithm. Third, the objects of interest are detected from the obtained clusters. Beside that, it gives an improvement in the Interval Type-2 Fuzzy Subtractive Clustering algorithm to reduce the time consuming. In theory, it is at least 3700 times better than the original one, and approximate 980100 in practice on our depth frames. The results conducted on frames demonstrate that the distance from the camera to objects retrieved is exact enough for indoor robot navigation problems.