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Experimental Comparison of Techniques for Localization and Mapping Using A Bearing-Only Sensor

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journal contribution
posted on 01.01.2000, 00:00 by Matthew Deans, Martial Hebert
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.




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