Fast vision-based object segmentation for natural landmark detection on Indoor Mobile Robot

Abstract

In this paper a new fast vision-based Object Segmentation technique by extracting straight line features from the indoor scenes is proposed. An indoor space scene always contains natural structures like doors, walls, ceilings and floor which have clear straight lines and large homogeneous color surfaces that can be stably detected to form the objects. The objects bounded with lines are very suitable for Indoor Mobile Robot to quickly detect, save as natural landmarks and use in visual SLAM. Compared with the POI (point of interest) features like Harris corner, the line features not only are more robust to changes of scale and illumination, but also can provide more structural information of the indoor environment. This algorithm works in real time and is stable against variation of illumination. The main idea of the method is combining straight lines to form lots of convex polygons. Polygons with homogenous color are kept and adjacent polygons with similar color are merged by a merge test process. A fast line segmentation and fitting method is proposed to improve the line detection efficiency and half edge structure is added to simplify the polygon generation process. Finally experiment results demonstrate the accuracy and robustness of the proposed algorithm in real indoor environments.