An ActiveVision System for Recognition of Pre-Marked Objects

Abstract -In this paper, a new 3D-object-recognition method for robotic assembly workcells is presented. The proposed method is focused on two basic concepts, namely active vision and object preconditioning. The paper will briefly present the main aspects of the proposed system. The Proposed Recognition Method [l] Herein, it is proposed to model an object using only a small number of 2D topologically-distinct perspective views. These are referred to as standardviews, each with a corresponding standard-view-axis. For successful recognition purposes, the input image of' an object must be one of its standard perspective views. Thus, a mobile camera is used, such that its optical axis can be aligned with one of the standardview-axes of the object in order to acquire a standardview. Then, the matching process is performed between h e acquired 2D standard-view of the object and the library of 2D standard-views of a set of objects. To enable the vision system to acquire standardviews, standard-view-axes must be pre-defined. This can be accomplished by defining a local surface normal for each distinct view of an object. The local surface normals can be defined by pre-marking the objects using circular markers. In the context of the above scheme, active vision is used for two purposes: (1) Acquiring only specific views of an object (i.e., standard views) by controlling the external parameters of the camera; and (2) Acquiring additional images (standard views) if needed by virtuc of the possibility that the recognition process is not successful after the analysis of the first image, either due to significant distortion and noise, or, insufficient visual information in the image initially acquired. On the other hand, object pre-marking, servcs the following purposes: (1) Specifying a set of object surfaces to be viewed; (2) Defining a local surface normal --a standard axis-of-view --(which can be cstimatcd from the shape of a marker in the imagc plane); and (3) Conveying local 3D orientation and 3D position of a surface of an object, which can be subsequently used for 3D-location estimation of the objcct with respect to a refercnce frame. Based on the above scheme, the major steps for the identification and 3D-location estimation of a premarked object can be listed as follows: In the context of on-line issues and system implementation, the following aspects of the new technique have been addressed: a sequential distortioncompensation procedure, marker boundary detection to a sub-pixel accuracy, elliptical parameter estimation [71, and 3D-location estimation of circular markers [8]. For a complete presentatior? of each of the above issues and the proposed solution method and experimental results, please refer to the corresponding refcrences indicated above. An Experimental Prototype of the Active-Vision System [3] In an attempt to verify the validity and performance of the presented active object recognition technique, a prototype of the system was developed. The prototype is able to recognize the idcntity of manufactured objects which appear randomly oriented in thc field of view of a camera, provided that the standard views of all objects are available and stored in a standard-view database. In this particular implementation, the functions of the active object recognition are distributed between a loosely-coupled vision subsystem and robot-control subsystem. The system is an integration of the follow