<title>Comparing subset-convergent and variable-depth local search on perspective-sensitive landmark recognition problems</title>

摘要

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.