The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Mobile robots and robot teams can leverage multiple views of a scene to improve the accuracy of their maps. However non-uniform noise persists even when each sensor's pose is known, and the uncertain correspondence between detections from different views complicates easy "multiple view object detection." We present an algorithm based on Expectation/Maximization (EM) Clustering that permits a principled fusion of the views without requiring an explicit correspondence search. We demonstrate the use of this algorithm to improve mapping performance of robots in simulation and in the field.

Keywords

Computer Vision, Mapping, Exploration Robotics

Notes

Sponsor: NASA

Grant ID: NNG0-4GB66G, NAG5-12890

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Science Autonomy and Life in the Atacama

Number of pages: 6

Text Reference

David R. Thompson and David Wettergreen, "Multiple-Object Detection in Natural Scenes with Multiple-View Expectation Maximization Clustering," International Conference on Intelligent Robots and Systems (IROS '05), August, 2005, pp. 448 - 453.

BibTeX Reference

@inproceedings{Thompson_2005_5113,
   author = "David R Thompson and David Wettergreen",
   title = "Multiple-Object Detection in Natural Scenes with Multiple-View Expectation Maximization Clustering",
   booktitle = "International Conference on Intelligent Robots and Systems (IROS '05)",
   pages = "448 - 453",
   month = "August",
   year = "2005",
}

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