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Volume 23 Issue 12 - Publication Date: 1 December 2004
 
Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework
 
Michael Bosse Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA Paul Newman Department of Engineering Science, University of Oxford, Oxford, UK, John Leonard and Seth Teller Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
 
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.
 
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Extension
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Description
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Example One: Processing of the Killian Court data set using feature-based local SLAM processing laser and sonar data concurrently (figure 15). (44.0 MB)
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Example Two: Raw Killian Court data set [zip file] (8.3 MB)
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Example Three: Processing of the CMU mines data set using scan-matching as the local SLAM algorithm (Figure 18) (37.0 MB)
 
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