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C. Siagian, L. Itti, Storing and Recalling Information for Vision Localization, In: IEEE International Conference on Robotics and Automation (ICRA), Pasadena, California, pp. 1848-1855, May 2008. [2008 acceptance rate: 43%] (Cited by 9)
Abstract: In implementing a vision localization system, a crucial issue to consider is how to efficiently store and recall the necessary information, so that the robot is not only able to accurately localize itself, but does so in a timely manner. In the presented system, we discuss a strategy to minimize the amount of stored data by analyzing the strengths and weaknesses of several cooperating recognition modules and by using them through a prioritization scheme which orders the data entries from the most likely to match to the least likely. We validate the system through a series of experiments in three large scale outdoor environments: a building complex (126x180ft. area, 3583 testing images), a vegetation-filled park (270x360ft. area, 6006 testing images), and an open-field area (450x585ft. area, 8823 testing images) - each with its own set of challenges. Not only is the system able to localize in these environments (on average 3.46ft., 6.55ft., 12.96ft. of error, respectively), it does so while searching through only 7.35%, 3.50%, and 6.12% of all the stored information, respectively.
Themes: Model of Bottom-Up Saliency-Based Visual Attention, Scene Understanding
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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