Tree Trunks as Landmarks for Outdoor Vision SLAM

Abstract

Simultaneous Localization and Mapping (SLAM) of robots is the process of building a map of the robot milieu, while simultaneously localizing the robot inside that map. Cameras have been recently proposed, as a replacement for laser range finders, for the purpose of detecting and localizing landmarks around the navigating robot. Vision SLAM is either Interest Point (IP) based, where landmarks are images saliencies, or object-based where real objects are used as landmarks. The contribution of this paper is two prong: first, it details an approach based on Perceptual Organization (PO) to detect and track trees in a sequence of images, thereby promoting the use of a camera as a viable exteroceptive sensor for object-based SLAM; second,it demonstrates the superiority of the suggested PO system over two appearance-based algorithms in segmenting trees from difficult settings. Experiments conducted on a database of 873 images containing approximately 2008 tree trunks, show that the proposed system correctly classifies trees at 81 % with a false positive rate of 30%.