1 November 1992 Comparing subset-convergent and variable-depth local search on perspective-sensitive landmark recognition problems
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Proceedings Volume 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision; (1992) https://doi.org/10.1117/12.131525
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
Abstract
An intelligent robot with a camera and a partial model of its environment should be able to determine where it is from what it sees. This goal, landmark based navigation, can be realized using geometric object recognition algorithms. An important problem that arises in the development of such algorithms concerns the role of full 3-D perspective projection. Much of the work on object recognition has focused upon simplified problems which are essentially 2- D. One such simplification uses weak-perspective: to test the alignment of matched features object models are rotated, translated, and scaled in the image plane. At increased computational cost, full-perspective can be incorporated into recognition using a family of probabilistic optimization procedures based upon local search. This paper considers two specific algorithms from this family: subset-convergent and variable-depth local search. Both approaches reliably recognize landmarks even when landmark appearance is sensitive to perspective. Results presented here suggest the relatively simpler variable-depth algorithm is preferable when errors in the robot pose estimate are smaller, but that at some point as uncertainty in the initial pose estimate increases the more sophisticated subset-convergent algorithm becomes preferable.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Ross Beveridge, J. Ross Beveridge, "Comparing subset-convergent and variable-depth local search on perspective-sensitive landmark recognition problems", Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131525; https://doi.org/10.1117/12.131525
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