This work presents a landmark detection system for a walking robot, which has to operate in unknown unstructured outdoor environments. Most landmark detection approaches are not adequate for this application, since they rely on either structured information or a priori knowledge about the landmarks. Instead, the proposed system makes use of visual saliency concepts stemming from studies of animal and human perception. Thus, biologically inspired opponent features (in color and orientation) are searched for at different resolution levels. The implementation, however, does not try to mimic nature, but rather to be as computationally efficient as possible. Thus, salient image regions ranging from relatively small to big sizes are detected using multiscale comparison techniques, based on pyramidal filtering. The experimental results obtained show that visual saliency permits detecting reliable natural landmarks without a priori knowledge about their characteristics or location.