Hybrid weak-perspective and full-perspective matching

The hybrid algorithm reliably recovers the true pose of the robot, and like the weak-perspective algorithm, it runs 5 to 10 faster than the full-persistive algorithm.

Abstract

Full-perspective mappings between 3-D objects and 2-D images are more complicated than weak-perspective mappings, which consider only rotation, translation, and scaling. Therefore, in 3-D model-based robot navigation, it is important to understand how and when full-perspective must be taken into account. A probabilistic combinatorial optimization algorithm is used to search for an optimal match between 3-D landmarks and 2-D image features. Three variations are considered. A weak-perspective algorithm rotates, translates, and scales an initial 2-D projection of the 3-D landmark. A full perspective selects a most promising alternative, but then updates the pose and reprojects the landmark. Like the full-perspective algorithm, the hybrid algorithm reliably recovers the true pose of the robot, and like the weak-perspective algorithm, it runs 5 to 10 faster than the full-perspective algorithm.>