Off-line planning for on-line object localization

Abstract

MIT Artificial Intelligence Laboratory Cambridge, MA 02139 Abstract. Many robot applications require using sensors to locate objects whose initial pose is constrained but not exactly known. Most techniques for object localization as- sume that the object's pose is completely unknown. This paper describes a simple method for localizing known ob- jects in a scene. We describe how an off-line computation that exploits constraints on the object's expected pose can be used to reduce the expected time for the on-line compu- tation to localize the object. The objects treated here are modeled as polyhedra that, in principle, can have up to six degrees of positional freedom relative to the sensors. the object. An important subgoal is that both the on-line and off-line methods should be simple enough to be easily implemented. The method described here can be applied to both two- dimensional and three-dimensional sensing situations. In the two-dimensional case, objects have only three degrees of positional freedom relative to the sensor (two transia- tional and one rotational). In this case, the sensors (and their pre-processors) are assumed to compute edges, that is, line segments in the scene. In the three-dimensional case, objects have up to three translational and three rotational degrees of .freedom. In this case, the sensors (and their pre-processors) are assumed to compute planar patches in the scene. We do not deal with the general case in which only two-dimensional data is available but the object has more than three degrees of freedom. For the sake of brevity, we limit our discussion to the three-dimensional case; the specialization to two-dimensions is straightforward.