The reliability of the method was demonstrated through a series of examples, each increasing the complexity in terms of the observed scene and , the sample spacing. Scene I demonstrated the feasibility of the method, and considered performance under a variety of parameterisations. Pose estimation using only the edge distribution was also considered, but demonstrated some difficulty at estimating pose from the ``appearance'' of the edges. Applying the method to Scene II demonstrated the effects of reducing the sampling density and provided a slightly more complex scene, with excellent results. Pose estimation with Scene III demonstrated that the method can be extended to a larger, more realistic environment with good results. In addition, some key problems were identified for implementing the method in a working environment. Scene IV attempted to tackle some of the problems identified in Scene III, particularly that of obtaining reliable ground truth. The results of this experiment were very good. In addition, Scene IV was used to demonstrate the reliability of the method under changes in the scene - an aspect which gives the method a significant advantage over many other localisation solutions, particularly those that train neural networks using global image statistics. Finally, Scene IV was used to experiment with a consistency measure which can be used to recover an unknown orientation given a database which is trained in a fixed direction.