T. Frohlinghaus and J.M. Buhmann. Real-time phase-based stereo for a mobile robot. In Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots. IEEE Computer Society Press, 1996. Home/Search Document Details and Download
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Learning Maps for Indoor Mobile Robot Navigation - Thrun (1998) (41 citations) (Correct)
....described here. robot competition is shown in Figure 9. This map contains an open area, which was intentionally created by the competition organizers to test the robot s ability to navigate in large open spaces [94] In [103] occupancy maps are constructed using stereo vision for depth estimation [37,38]. As shown there, sonar and stereo information have somewhat orthogonal sensor characteristics and thus can complement each other. Position control based on odometry and map correlation alone (items 1 and 2 above) works well if the robot travels through mapped terrain [99] but fails to localize ....
T. Frohlinghaus and J.M. Buhmann. Real-time phase-based stereo for a mobile robot. In Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots. IEEE Computer Society Press, 1996.
Map Learning and High-Speed Navigation in RHINO - Thrun, Bücken, Burgard, Fox, .. (1996) (52 citations) (Correct)
....simultaneously from different spatial viewpoints. Stereo images are transmitted via a radio link to a Datacube, a special purpose computer for image processing. Like the human visual apparatus, stereo images can be used to compute depth information (i.e. proximity) Put shortly, our approach [20, 21] analyzes stereo images for co occurrences of vertical edges. By analyzing the disparity of vertical edges found in both images the proximity of obstacles is estimated, and projected onto a two dimensional occupancy grid. Figure 3 illustrates the stereo image analysis. Images are first ....
T. Frohlinghaus and J.M. Buhmann. Real-time phase-basedstereo for a mobile robot. In Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots. IEEE Computer Society Press, 1996.
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