| Volume 23 Issue 12 - Publication Date: 1 December 2004 |
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| Simultaneous Localization and
Map Building in Large-Scale Cyclic Environments Using the Atlas Framework |
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| 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 |
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| 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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| Multimedia Key |
= Video |
 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
1 |
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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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