The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We present a comparison of an extended Kalman filter and an adaptation of bundle adjustment from computer vision for mobile robot localization and mapping using a bearing-only sensor. We show results on synthetic and real examples and discuss some advantages and disadvantages of the techniques. The comparison leads to a novel combination of the two techniques which results in computational complexity near Kalman filters and performance near bundle adjustment on the examples shown.

Notes

Text Reference

Matthew Deans and Martial Hebert, "Experimental Comparison of Techniques for Localization and Mapping using a Bearings Only Sensor," Proc. of the ISER '00 Seventh International Symposium on Experimental Robotics, December, 2000.

BibTeX Reference

@inproceedings{Deans_2000_3453,
   author = "Matthew Deans and Martial Hebert",
   title = "Experimental Comparison of Techniques for Localization and Mapping using a Bearings Only Sensor",
   booktitle = "Proc. of the ISER '00 Seventh International Symposium on Experimental Robotics",
   month = "December",
   year = "2000",
}

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