Computing the Sensory Uncertainty Field of A Vision-based Localization Sensor
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Computing the Sensory Uncertainty Field of a Vision-based Localization Sensor.
IEEE Trans. On Robotics and Automation, 17(3):258-268, 2001
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Abstract
Recently it has been recognized that robust motion planners should take into account the varying performance of localization sensors across the configuration space. Although a number of works have shown the benefits of using such a performance map, the work on actual computation of such a performance map has been limited and has addressed mostly range sensors. Since vision is an important sensor for localization, it is important to have performance maps of vision sensors. In this paper we present a method for computing the performance map of a vision-based sensor. We compute the map and show that it accurately describes the actual performance of the sensor, both on synthetic and real images. The method we present involves evaluating closed form formulas and hence is very fast. Using the performance map computed by our method for motion planning and for devising sensing strategies will contribute to more robust navigation algorithms.
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Bibtex Entry
@article{AdamRS01a-c,
title = {Computing the Sensory Uncertainty Field of a Vision-based Localization Sensor},
author = {Amit Adam and Ehud Rivlin and Ilan Shimshoni},
year = {2001},
month = {June},
journal = {IEEE Trans. On Robotics and Automation},
volume = {17},
number = {3},
pages = {258-268},
keywords = {Motion Planning; Performance evaluation; Uncertainty; Vision based Localization},
abstract = {Recently it has been recognized that robust motion planners should take into account the varying performance of localization sensors across the configuration space. Although a number of works have shown the benefits of using such a performance map, the work on actual computation of such a performance map has been limited and has addressed mostly range sensors. Since vision is an important sensor for localization, it is important to have performance maps of vision sensors. In this paper we present a method for computing the performance map of a vision-based sensor. We compute the map and show that it accurately describes the actual performance of the sensor, both on synthetic and real images. The method we present involves evaluating closed form formulas and hence is very fast. Using the performance map computed by our method for motion planning and for devising sensing strategies will contribute to more robust navigation algorithms.}
}