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• W2097012286 startingPage "1113" @default.
• W2097012286 abstract "In this paper we describe Atlas, a hybrid metrical/topological approach to simultaneous localization and mapping (SLAM) that achieves efficient mapping of large-scale environments. The representation is a graph of coordinate frames, with each vertex in the graph representing a local frame and each edge representing the transformation between adjacent frames. In each frame, we build a map that captures the local environment and the current robot pose along with the uncertainties of each. Each map’s uncertainties are modeled with respect to its own frame. Probabilities of entities with respect to arbitrary frames are generated by following a path formed by the edges between adjacent frames, computed using either the Dijkstra shortest path algorithm or breath-first search. Loop closing is achieved via an efficient map-matching algorithm coupled with a cycle verification step. We demonstrate the performance of the technique for post-processing large data sets, including an indoor structured environment (2.2 km path length) with multiple nested loops using laser or ultrasonic ranging sensors." @default.
• W2097012286 created "2016-06-24" @default.
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• W2097012286 date "2004-12-01" @default.
• W2097012286 modified "2023-09-27" @default.
• W2097012286 title "Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework" @default.
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• W2097012286 doi "https://doi.org/10.1177/0278364904049393" @default.
• W2097012286 hasPublicationYear "2004" @default.
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