The results of recent studies on the possibility of spatial localization from panoramic images have shown good prospects for view-based methods. The major advantages of these methods are a wide field-of-view, capability of modeling cluttered environments, and flexibility in the learning phase. The redundant information captured in similar views is efficiently handled by the eigenspace approach. However, the standard approaches are sensitive to noise and occlusion. In this paper, we present a method of view-based localization in a robust framework that solves these problems to a large degree. Experimental results on a large set of real panoramic images demonstrate the effectiveness of the approach and the level of achieved robustness.