Abstract
Computer vision techniques can enhance landmark-based navigation by better utilizing online photo collections. We use spatial reasoning to compute camera poses, which are then registered to the world using GPS information extracted from the image tags. Computed camera pose is used to augment the images with navigational arrows that fit the environment. We develop a system to use high-level reasoning to influence the selection of landmarks along a navigation path, and lower-level reasoning to select appropriate images of those landmarks. We also utilize an image matching pipeline based on robust local descriptors to give users of the system the ability to capture an image and receive navigational instructions overlaid on their current context. These enhancements to our previous navigation system produce a more natural navigation plan and more understandable images in a fully automatic way.
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Hile, H., Grzeszczuk, R., Liu, A., Vedantham, R., Košecka, J., Borriello, G. (2009). Landmark-Based Pedestrian Navigation with Enhanced Spatial Reasoning. In: Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., Tobe, Y. (eds) Pervasive Computing. Pervasive 2009. Lecture Notes in Computer Science, vol 5538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01516-8_6
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