Framework for Natural Landmark-based Robot Localization

In this paper we present a framework for vision-based robot localization using natural planar landmarks. Specifically, we demonstrate our framework with planar targets using Fern classifiers that have been shown to be robust against illumination changes, perspective distortion, motion blur, and occlusions. We add stratified sampling in the image plane to increase robustness of the localization scheme in cluttered environments and on-line checking for false detection of targets to decrease false positives. We use all matching points to improve pose estimation and an off-line target evaluation strategy to improve a priori map building. We report experiments demonstrating the accuracy and speed of localization. Our experiments entail synthetic and real data. Our framework and our improvements are however more general and the Fern classifier could be replaced by other techniques.