Object (computer science)

About: Object (computer science) is a research topic. Over the lifetime, 106024 publications have been published within this topic receiving 1360115 citations. The topic is also known as: obj & Rq.

Device, system and method for accurate printing of three dimensional objects

Abstract: A system and method for building three dimensional objects may adjust data used to build the objects to, for example, improve quality or correct for defects. A printer may, for example, accept data representing the object and modify the data according to an adjustment parameter; the printer may build the object according to the parameter. The parameter may be, for example, fixed in the printer, user entered, and/or calculated by the printer. In one embodiment, a support pedestal may be built of deposited material before the object is built.

Context sensitive menu system/menu behavior

Abstract: A method and system are described for a computer system for retrieving and presenting a set of commands in the form of a pop up context menu for a selected object. The context menu is displayed in the proximity of the selected object and is determined primarily by the class of the selected object and secondarily by the particular container in which the selected object resides at the time of selection. The context menu displays a number of useful features which enable the user to quickly and easily invoke commands upon the selected object.

RF-compass: robot object manipulation using RFIDs

Abstract: Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the object's orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches).This paper presents RF-Compass, an RFID-based system for robot navigation and object manipulation. RFIDs are low-cost and work in non-line-of-sight scenarios, allowing them to address the limitations of existing solutions. Given an RFID-tagged object, RF-Compass accurately navigates a robot equipped with RFIDs toward the object. Further, it locates the center of the object to within a few centimeters and identifies its orientation so that the robot may pick it up. RF-Compass's key innovation is an iterative algorithm formulated as a convex optimization problem. The algorithm uses the RFID signals to partition the space and keeps refining the partitions based on the robot's consecutive moves.We have implemented RF-Compass using USRP software radios and evaluated it with commercial RFIDs and a KUKA youBot robot. For the task of furniture assembly, RF-Compass can locate furniture parts to a median of 1.28 cm, and identify their orientation to a median of 3.3 degrees.

System and method for creating reusable components in an object-oriented programming environment

Abstract: A novel object called the Data Object is used to represent different information types in a system in a singular way in an object-oriented programming environment. The Data Object stores the value of the information and a reference to an object that handles that specific type of information. All requests on the Data Object are forwarded to this handler. The interface allows programmers to query the structure of the information, place constraints on values and names of the parts. This makes it possible to write code that performs a service that adapts itself to whatever Data Object it gets. The use of a Data Object significantly minimizes the coupling between modules and make reusable code easier to develop because each object class in the object-oriented software system is now coupled only to the Data Object class.

Depth Mapping Based on Pattern Matching and Stereoscopic Information

Abstract: A method for depth mapping includes projecting a pattern of optical radiation onto an object. A first image of the pattern on the object is captured using a first image sensor, and this image is processed to generate pattern-based depth data with respect to the object. A second image of the object is captured using a second image sensor, and the second image is processed together with another image to generate stereoscopic depth data with respect to the object. The pattern-based depth data is combined with the stereoscopic depth data to create a depth map of the object.