In automation and handling engineering, supplying work pieces between different stages along the production process chain is of special interest. Often the parts are stored unordered in bins or lattice boxes and hence have to be separated and ordered for feeding purposes. An alternative to complex and spacious mechanical systems such as bowl feeders or conveyor belts, which are typically adapted to the parts’ geometry, is using a robot to grip the work pieces out of a bin or from a belt. Such applications are in need of reliable and precise computer-aided object detection and localization systems. For a restricted range of parts, there exists a variety of 2D image processing algorithms that solve the recognition problem. However, these methods are often not well suited for the localization of randomly stored parts. In this paper we present a fast and flexible 3D object recognizer that localizes objects by identifying primitive features within the objects. Since technical work pieces typically consist to a substantial degree of geometric primitives such as planes, cylinders and cones, such features usually carry enough information in order to determine the position of the entire object. Our algorithms use 3D best-fitting combined with an intelligent data pre-processing step. The capability and performance of this approach is shown by applying the algorithms to real data sets of different industrial test parts in a prototypical bin picking demonstration system.
In this paper we describe a real-time 3D environment model for obstacle detection and collision avoidance with
a mobile service robot. It is fully integrated in the experimental platform DESIRE. Experiments show, that all
components perform well and allow for reliable and robust operation of a mobile service robot with actuating
capabilities in the presence of obstacles.
The industry is in need of reliable, computer aided object recognition and localization systems in automation
and handling engineering. One possible application is bin picking, i.e. the task of grasping work pieces out of a
storage container with a robot. Therefore, the parts do not have to be ordered or semi-ordered but can be totally
unordered. 2D image processing techniques often can not perform such sophisticated tasks since the gray scale or
color information provided is just not enough. An alternative is the examination of the other dimension. In this
paper we discuss a novel approach to a 3D object recognizer that localizes objects by looking at the primitive
features within the objects. The basic idea of the system is that the geometric primitives usually carry enough
information to make possible proper object recognition and localization. The algorithms use 3D best-fitting
combined with clever 2.5D preprocessing. The feasibility of the approach is demonstrated and tested by means
of a prototypical bin picking system. The time taken to recognize and localize an object is < 0.5 sec., and the
accuracy of the result is in the order of magnitude of the measurements inaccuracy, < 0.5 mm.