This Article Full Text (PDF) Multimedia References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (41) Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Bosse, M. Articles by Teller, S. Search for Related Content Social Bookmarking                         What's this?

Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, ifni{at}mit.edu

Paul Newman

Department of Engineering Science, University of Oxford, Oxford, UK

John Leonard

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA

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.

Key Words: mobile robots • SLAM • scaling • navigation and mapping • ATLAS • cyclic environments

The International Journal of Robotics Research, Vol. 23, No. 12, 1113-1139 (2004)
DOI: 10.1177/0278364904049393


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				M. Bosse and R. Zlot Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM The International Journal of Robotics Research, June 1, 2008; 27(6): 667 - 691. [Abstract] [PDF]

				A. Diosi and L. Kleeman Fast Laser Scan Matching using Polar Coordinates The International Journal of Robotics Research, October 1, 2007; 26(10): 1125 - 1153. [Abstract] [PDF]

				S. Thrun and M. Montemerlo The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures The International Journal of Robotics Research, May 1, 2006; 25(5-6): 403 - 429. [Abstract] [PDF]