This paper investigates the effectiveness of using lampposts, which are commonly found in University campus environments with high frequency, as landmarks in a 2D LIDAR based Simultaneous Localization and Mapping (SLAM) framework. Lampposts offer a number of benefits compared to other forms of landmarks. Their unique spatial signature makes it possible to design effective algorithms to extract them. They have a very small spatial size. Their use removes the challenge of determining a corresponding location between difference views. This represents a major challenge if larger objects are used as landmarks. The proposed SLAM algorithm contains three stages. Firstly LIDAR segmentation is performed. Next each object is input to a binary classifier which determines objects with a high probability of corresponding to lampposts. Finally these extracted lampposts are input to an Iterative Closest Point (ICP) based SLAM algorithm. The ICP algorithm used is an extension of the traditional ICP algorithm and filters associations due to noise. Results achieved by the proposed system were very positive. An accurate map of a university’s lampposts was created and localization, when compared to GPS ground-truth, was very accurate.