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Abstract

Accomplishing simultaneous localization and mapping (SLAM) in very large city environments is a great challenge because of theoretical and practical issues on computational complexity, dynamic environment, representation and data association. In this paper, we describe practical algorithms for dealing with the representation issues. Feature-based, grid-based and direct methods are integrated into the framework of the hierarchical object based representation. The sampling and correlation based range image matching algorithm is developed to tackle the problem arising from uncertain, sparse and featureless data in outdoor environments. Experimental results of a 800 meter x 600 meter neighborhood demonstrate the feasibility of city-sized SLAM.

Notes

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

Associated Lab(s) / Group(s): NavLab

Associated Project(s): Transit Bus Collision Warning Systems and Simultaneous Localization and Mapping with Detection, Tracking, and Classification of Moving Objects

Number of pages: 7

Text Reference

Chieh-Chih Wang and Chuck Thorpe, "A Hierarchical Object Based Representation for Simultaneous Localization and Mapping," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September, 2004.

BibTeX Reference

@inproceedings{Wang_2004_4721,
   author = "Chieh-Chih Wang and Chuck Thorpe",
   title = "A Hierarchical Object Based Representation for Simultaneous Localization and Mapping",
   booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
   month = "September",
   year = "2004",
}

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