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Carnegie Mellon Robotics Institute

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Abstract

Mobile robot localization from large-scale appearance mosaics has been showing increasing promise as a low-cost, high-performance and infrastructure free solution to vehicle-guidance in man-made environments. The generation of the globally consistent high-resolution mosaics crucial to this procedure suffers from the same problem of loop-closure in cyclic environments that is commonly encountered in all map-building procedures. This paper presents a batch solution to the problem of reliably generating globally consistent mosaics at low computational cost, that simultaneously exploits the topological constraints among the observations and minimizes the total residual in observed features. An extension to a general scalable framework that facilitates an incremental online mapping strategy is also presented, along with results using simulated data and from real indoor environments.

Notes

Associated Center(s) / Consortia: National Robotics Engineering Center

Associated Project(s): Material Transport

Number of pages: 6

Text Reference

Ranjith Unnikrishnan and Alonzo Kelly, "A Constrained Optimization Approach to Globally Consistent Mapping," 2002 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems (IROS '02), October, 2002, pp. 564-569.

BibTeX Reference

@inproceedings{Unnikrishnan_2002_4098,
   author = "Ranjith Unnikrishnan and Alonzo Kelly",
   title = "A Constrained Optimization Approach to Globally Consistent Mapping",
   booktitle = "2002 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems (IROS '02)",
   pages = "564-569",
   month = "October",
   year = "2002",
   volume = "1",
}

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