LAUSR

ABSTRACT In this paper, a landmark selection and tracking approach is presented for mobile robot navigation in natural environments, using textural distinctiveness-based saliency detection and spatial information acquired from stereo data. The presented method focuses on achieving high robustness of tracking rather than self-positioning accuracy. The landmark selection method is designed to select a small amount of the most salient feature points in a wide variety of sparse unknown environments to ensure successful matching. Landmarks are selected by an iterative algorithm from a textural distinctiveness-based saliency map extended with spatial information, where a repulsive potential field is created around the position of each already selected landmark for better distribution in order to increase robustness. The template matching of landmarks is aided with visual odometry-based motion estimation. Other robustness increasing strategies includes estimating landmark positions by unscented Kalman filters as well as from surrounding landmarks. Experimental results show that the introduced method is robust and suitable for natural environments. GRAPHICAL ABSTRACT