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Carnegie Mellon Robotics Institute

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Abstract

Long range navigation by unmanned ground vehicles continues to challenge the robotics community. Efficient navigation requires not only intelligent on-board perception and planning systems, but also the effective use of prior knowledge of the vehicle? environment. This paper describes a system for supporting unmanned ground vehicle navigation through the use of heterogeneous overhead data. Semantic information is obtained through supervised classification, and vehicle mobility is predicted from available geometric data. This approach is demonstrated and validated through over 50 kilometers of autonomous traversal through complex natural environments.

Keywords

overhead, mobile robot, navigation, mapping, obstacle detection

Notes

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

Associated Project(s): UGCV PerceptOR Integrated

Number of pages: 8

Text Reference

David Silver, Boris Sofman, Nicolas Vandapel, J. Andrew (Drew) Bagnell, and Anthony (Tony) Stentz, "Experimental Analysis of Overhead Data Processing To Support Long Range Navigation," IEEE International Conference on Intelligent Robots and Systems (IROS), October, 2006, pp. 2443 - 2450.

BibTeX Reference

@inproceedings{Silver_2006_5520,
   author = "David Silver and Boris Sofman and Nicolas Vandapel and J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz",
   title = "Experimental Analysis of Overhead Data Processing To Support Long Range Navigation",
   booktitle = "IEEE International Conference on Intelligent Robots and Systems (IROS)",
   pages = "2443 - 2450",
   month = "October",
   year = "2006",
}

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