Objects Segmentation Using Multiple Features for Robot Navigation on Outdoor Environment.

This paper presents the method to recognize objects for autonomous robot navigation in outdoor environment. The proposition of the method segments from an image taken by a moving robot in an outdoor environment. The method begins with object segmentation, which uses multiple features to obtain the object of segmented region. Multiple features are color, context information, line segments, edge, Hue Co-occurrence Matrix (HCM), Principal Components (PCs) and Vanishing Points (VPs). We model the objects of outdoor environment that define their characteristics individually. We segment the region as a mixture using the proposed features and methods. Objects can be detected when we combine predefined multiple features. Next, the stage classifies the object into natural and artificial ones. We detect sky and trees of natural objects. And we detect building of artificial objects. The last stage shows the combination of appearance and context information. We implement the result of object segmentation using multiple features through experiments.