Simultaneous Localization and Mapping (SLAM) based on visual information is a challenging problem. One of the main problems with visual SLAM is to find good quality landmarks, that can be detected despite noise and small changes in viewpoint. Many approaches use SIFT interest points as visual landmarks. The problem with the SIFT interest points detector, however, is that it results in a large number of points, of which many are not stable across observations. We propose the use of local symmetry to find regions of interest instead. Symmetry is a stimulus that occurs frequently in everyday environments where our robots operate in, making it useful for SLAM. Furthermore, symmetrical forms are inherently redundant, and can therefore be more robustly detected. By using regions instead of points-of-interest, the landmarks are more stable. To test the performance of our model, we recorded a SLAM database with a mobile robot, and annotated the database by manually adding ground-truth positions. The results show that symmetrical regions-of-interest are less susceptible to noise, are more stable, and above all, result in better SLAM performance.