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Vision loss is rarely absolute; most people referred to as blind have some form of residual vision. This residual vision can create a confusing view of the world. In this thesis, I explore how a camera system and a wearable display can augment an image of the word to improve its visibility and understanding to a visually impaired wearer. I focus on two crucial parts of life; recognising faces and reading. In the first part of this thesis, I design and validate a new face perception test to assess different image enhancement forms. I used this new test to measure the impact of image enhancement on a visually impaired population (n=64). The results found that approximately 40% of participants showed a significant improvement in test scores. Performance was related to reading acuity and lower spatial frequency loss. I then develop and integrate a complete real-time image processing solution for face perception enhancement with an augmented reality display, demonstrating a practical application of the image enhancements. The second part of this thesis's focus is reading. I compare paper-based reading performance against reading with a virtual reality (VR) display with a visually impaired population (n=53). I further assess the impact of different colours on reading with the VR display. The results found no difference in reading performance, comparing VR to paper- based. The colour of text and background had a significant effect, with 75% of participants having a preferred colour combination. A participant with a higher overall magnitude of colour vision loss is more likely to benefit from white text on a black background than black text on a yellow background. Augmenting an individual's residual vision can improve the quality of information they receive about the visual world. While not universal, this improvement has the potential to enhance a visually impaired person's quality of life significantly.

With the advent of ever smaller and more powerful portable computing devices, and ever smaller cameras, wearable computing is becoming more feasible. The ever increasing numbers of augmented reality applications are allowing users to view additional data about their world overlaid on their world using portable computing devices. The main aim of this research is to enable a user of a wearable robot to explore large environments automatically viewing augmented reality at locations and on objects of interest. To implement this research a wearable visual robotic assistant is designed and constructed. Evaluation of the different technologies results in a final design that combines a shoulder mounted self stabilizing active camera, and a hand held magic lens into a single portable system. To enable the wearable assistant to locate known objects, a system is designed that combines an established method for appearance-based recognition with one for simultaneous localization and mapping using a single camera. As well as identifying planar objects, the objects are located relative to the camera in 3D by computing the image-to-database homography. The 3D positions of the objects are then used as additional measurements in the SLAM process, which routinely uses other point features to acquire and maintain a map of the surroundings, irrespective of whether objects are present or not. The monocular SLAM system is then replaced with a new method for building maps and tracking. Instead of tracking and mapping in a linear frame-rate driven manner, this adopted method separates the mapping from the tracking. This allows higher density maps to be constructed, and provides more robust tracking. The flexible framework provided by this method is extended to support multiple independent cameras, and multiple independent maps, allowing the user of the wearable two-camera robot to escape the confines of the desk top and explore arbitrarily sized environments. The final part of the work brings together the parallel tracking and multiple mapping system with the recognition and localization of planar objects from a database. The method is able to build multiple feature rich maps of the world and simultaneously recognize, reconstruct and localize objects within these maps. The object reconstruction process uses the spatially separated keyframes from the tracking and mapping processes to recognize and localize known objects in the world. These are then used for augmented reality overlays related to the objects.