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We present a graph-based SLAM approach, using monocular vision and odometry, designed to operate on computationally constrained platforms. When computation and memory are limited, visual tracking becomes difficult or impossible, and map representation and update costs must remain low. Our system constructs a map of structured views using only weak temporal assumptions, and performs recognition and...
Unknown environments pose a particular challenge for augmented reality applications because the 3D models required for tracking, rendering and interaction are not available ahead of time. Consequently, authoring of AR content must take place on-line. This work describes a set of techniques to simplify the online authoring of annotations in unknown environments using a simultaneous localisation and...
We present a monocular SLAM system that avoids inconsistency by coalescing observations into independent local coordinate frames, building a graph of the local frames, and optimizing the resulting graph. We choose coordinates that minimize the nonlinearity of the updates in the nodes, and suggest a heuristic measure of such nonlinearity, using it to guide our traversal of the graph. The system operates...
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