Highly accurate and robust relocalization or localization initialization ability is of great importance for autonomous vehicles (AVs). Traditional GNSS-based methods are not reliable enough in occlusion and multipath conditions. In this paper we propose a novel long-term semantic relocalization algorithm based on HD map and semantic features which are compact in representation. Semantic features appear widely on urban roads, and are robust to illumination, weather, view-point and appearance changes. Repeated structures, missed and false detections make data association (DA) highly ambiguous. To this end, a robust semantic feature matching method based on a new local semantic descriptor which encodes the spatial and normal relationship between semantic features is performed. Further, we introduce an accurate, efficient, yet simple outlier removal method which works by assessing the local and global geometric consistencies and temporal consistency of semantic matching pairs. The experimental results on our urban dataset demonstrate that our approach performs better in accuracy and robustness compared with the current state-of-the-art methods.