Abstract
An economical self-localization system which uses a monocular camera and a set of artificial landmarks is presented herein. The system represents the surrounding environment as a topological graph where each node corresponds to an artificial landmark and each edge corresponds to a relative pose between two landmarks. The edges are weighted based on an error metric (related to pose uncertainty) and a shortest path algorithm is applied to the map to compute the path corresponding to the least aggregate weight. This path is used to localize the camera with respect to a global coordinate system whose origin lies on an arbitrary reference landmark (i.e., the destination node of the path). The proposed system does not require a preliminary training process, as it builds and updates the map online. Experimental results demonstrate the performance of the system in reducing the global error associated with large-scale localization.
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Shaya, K., Mavrinac, A., Herrera, J.L.A., Chen, X. (2012). A Self-localization System with Global Error Reduction and Online Map-Building Capabilities. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_2
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DOI: https://doi.org/10.1007/978-3-642-33503-7_2
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