Recent years have seen the emergence of systems capable of tracking in real-time the 6-D pose of a moving camera whilst simultaneously building a structural map of the surrounding environment. Such vision based simultaneous localisation and mapping (SLAM) systems have huge potential in terms of providing low cost and flexible 3-D location sensing, capable of operating with agile hand held devices and in previously unseen environments. Applications are numerous, particularly in areas such as Augmented and Virtual Reality, in which positioning and tracking technology play a key role. However, a requirement for this potential to be realised is that these systems need to operate reliably and robustly in the presence of natural human motions, including rapid accelerations, erratic motion and sudden changes in viewpoint. Building in resistance to these real-world motion characteristics is the subject of this Thesis. Specifically, we investigate how to improve the data association stage of visual SLAM systems. Data association is the process of obtaining correct feature correspondences between any two images and is vital for stable operation. Previous approaches rely on simple but not very discriminative matching, leading to the selection of erroneou
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