Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity. We present a new sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle feature clouds on salient convex objects by sonar data association. The centroid of each circle cloud, called a sonar salient feature, is used as a natural landmark for EKF-based SLAM. After completing initial exploration in an unknown environment, SLAM-able areas with sonar salient features can be defined, and cylindrical objects are placed conveniently at weak SLAM-able areas as a supplemental environmental saliency to enhance SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar salient feature structure for EKF-based SLAM.