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Abstract

This paper addresses the problem of extracting information from range and color data acquired by a mobile robot in urban environments. Our approach extracts geometric structures from clouds of 3-D points and regions from the corresponding color images, labels them based on prior models of the objects expected in the environment - buildings in the current experiments - and combines the two sources of information into a composite labeled map. Ultimately, our goal is to generate maps that are segmented into objects of interest, each of which is labeled by its type, e.g., buildings, vegetation, etc. Such a map provides a higher-level representation of the environment than the geometric maps normally used for mobile robot navigation. The techniques presented here are a step toward the automatic construction of such labeled maps.

Notes

Sponsor: DARPA

Grant ID: NBCH1020014

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): 3D Computer Vision Group

Associated Project(s): MARS2020

Number of pages: 8

Text Reference

Caroline Pantofaru, Ranjith Unnikrishnan, and Martial Hebert, "Toward Generating Labeled Maps from Color and Range Data for Robot Navigation," Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October, 2003, pp. 1314 - 1321.

BibTeX Reference

@inproceedings{Pantofaru_2003_4580,
   author = "Caroline Pantofaru and Ranjith Unnikrishnan and Martial Hebert",
   title = "Toward Generating Labeled Maps from Color and Range Data for Robot Navigation",
   booktitle = "Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
   pages = "1314 - 1321",
   month = "October",
   year = "2003",
   volume = "2",
}

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