Topology‐adaptive modeling of objects using surface evolutions based on 3D mathematical morphology

In the paper is highlighted the industrial objects as cars, spacecraft, telescopes, plotters, lasers, elevators, etc., which are described by the mathematical model with double astatism. These models of control objects have a double pole in the origin of the coordinate axes. In order to tune the PID controller to the model of object with double astatism, it was elaborated the tuning algorithm based on the maximum stability degree method with iterations. It was done the computer simulation of the automatic control system with the respectively model of object and PID controller and it was done the analysis of the obtained performance. The advantages of the maximum stability degree method with iterations were highlighted by the reducing calculations and time, which lead to the procedure simplification of the PID controller tuning.

This study is to design a system of object-oriented model (OOM) reuse. This enhance the efficiency of software reuse through expressing object model utilizing cases, retrieving object model which is reused and developing object modeling procedure. We developed object model reuse system by case-based reasoning, called case-based object model reuse system (COMOR System). This system consists of four subsystems: the graphic user interface to interact with end-user, the object-oriented model management system to build a new object model, the case base to store the past object models, and the knowledge base to store object-oriented modeling and reusing knowledge.

In Amazon Robotic Challenge, we need to make unknown object's 3d model for recognizing and estimating 6D pose in limited time. The paper presents a unique autonomous system to build object's complete 3d model fast. This method is different with others for it can build object's model contains object's bottom side. We modify ElasticFusion to focus on building object's 3D model from object's up-side with assistant pose information from Aruco Markers. Then instead of using ICP or other merge algorithms, we merge object's upside point cloud and bottom-side point cloud easily by using Aruco Markers on transparent board. At last, we use octree and source point cloud to color poisson reconstruction mesh model to obtain object's final complete colored 3d model. Experiment results show that our approach can generate common object's complete model with acceptable size error in limited time.

This article proposes a SLAM algorithm referred to as RGBD-Sphere SLAM. The key innovation of this work is the prototypical system that demonstrates how formal models of 3D geometric shape and appearance can be transformed into generative classification models that detect and recognize these shapes. Object models are specified as shape programs in PSML; a custom-built procedural language for 3D object modeling. Classifiers for each PSML shape are created by simulating how instances of each shape manifest in real-world sensor data, e.g., color images and range images. The proposed RGBD-Sphere SLAM algorithm demonstrates a prototypical example of the system. Here, the PSML program specifies spherical 3D objects having diffuse surface albedos and distinct color appearances. A recognizer uses PSML models of each object's geometry and appearance to detect instances of these objects within streaming RGBD sensor data. The detected model parameters are then integrated into an RGBD SLAM algorithm; hence the name RGBD-Sphere SLAM. This article describes the PSML programs, the spherical detection and recognition algorithms used, and describes the impact this approach has for improving the performance of RGBD SLAM approaches by incorporating detected objects as landmarks. This is the first example of a prototypical system that externalizes the geometric and appearance modeling to a programming language from which a recognizer is created, and marks an important step towards enabling users to “program” their problem space and allow computers to transform the formal object models, as expressed in PSML, into customized classifiers suited for specific sensor suites, e.g., color imagery and depth imagery.

Implicit Surface-Based Geometric Fusion

A comparison of the object model underpinning open systems interconnection (OSI) network management standards with that being developed for object-oriented software development systems indicates a close alignment between the models. But the OSI object-model goes further: it supports notions of asynchronously occurring events, concurrency, and optionality of implementation. These features are not found in the OMG object-model, but neither are they precluded. Consequently, the way is paved for network-management product suppliers to use OMG-conformant software-development systems. >

Behavior of geographic objects holds a critical role in spatial databases. This, along with objects' position and space-varying attributes form a minimal set of concepts sufficient to capture spatial peculiarities in terms of the object-oriented rationale. We present the semantics and the graphical notation of a prototypical object-oriented model for the conceptual design of spatial databases: by extending the Object Model of the Object Modeling Technique to the Geographic Object Model, we show how the above three concepts fit naturally into any objectoriented tool. We augment this model with the constructs of spatial aggregation and spatial grouping to express the critical aspects of space-varying attributes, object boundary fuzziness and uncertainty, spatial relationships, and attribute generalization. Our proposal integrates the field- and object-based geographic views in one model. The principal idea behind this effort is the incorporation of a set of concepts into any semantic or object-oriented model in order to make them communicate at the conceptual level (semantic interoperability).

To understand a large and complicated system, its specifier and user should be able to separate essential concerns from irrelevant details. To do that, specification of behavioral semantics-as opposed to just signatures (i.e., syntax)-is essential. Abstract and precise specifications are the corner stones of object modeling. However, object modeling has usually considered only behavior of isolated objects. Real-world enterprises do not consist only of isolated objects. It is the joint behavior of associated objects that defines the object model of the enterprise and therefore makes the business understandable. The paper show how semantics of an information model (the result of analyzing the system) can be specified in an abstract and precise manner, by reusing programming methodology concepts, and how this approach to specification is used in standardization activities. >

Work objective is to develop a patentable design of locomotive traction drive mechanisms using the example of a suspension unit. Research methods: object modeling method, analytical methods for calculating dynamics of rolling stock. Research results and novelty: the design of the railway electric motor suspension unit, which will reduce costs in the course of maintenance and repair of a locomotive with a solid gear by increasing the reliability of the suspension unit parts, eliminating their wear, has been developed; a compensation coupling device has been devised. We will synthesize the object model of a technical system based on the identification of its typical structure by classifying technical systems. Conclusion: the application of the object modeling method together with the object model allows avoiding errors in the development of new designs of technical objects based on existing comparables and eliminates defects of the selected prototype. The use of the object modeling method makes it possible to synthesize patentable designs of the locomotive underframe mechanisms.

It is important to enable a robot to manipulate a target object that has no 3-D model information and is situated in an environment with other unknown objects nearby. This poses an open problem of how to combine perception and manipulation to enable the robot to build an appearance-based model of the target object on the spot to facilitate further manipulation of the object while avoiding the other unknown obstacles in the way. In this paper, we introduce an approach to enable a continuum manipulator, which is apt to maneuver through a crowded environment, to gradually build a 3-D surface model of the target object by moving an RGB-D sensor around the object while also detecting and avoiding surrounding unknown obstacles. Our approach interleaves perception and manipulation such that perception guides the manipulator movement, which in turn allows more perception of the target object for object model building and further manipulator motion. Our approach is characterized by a progressive strategy to register RGB-D images of the target object to build and extend a partial model of the object and the corresponding motion planning strategy for the continuum robot to carry out model building and avoid obstacles at the same time. To demonstrate the effectiveness of our approach, experiments on progressive model building of real objects from real RGB-D images are conducted, where a simulated continuum robot plans and executes its motion to carry the RGB-D camera around a target object for taking those images in an augmented reality setting.

The paper proposes a novel method of modeling multiobject scenes or objects of arbitrary topology by a marching method based on mathematical morphology operations with a variable-size structuring element. We assume that 3D data on the whole object surface has been acquired. A narrow-band stopping region is built by merging the neighborhood of every data point. A single close-surface surrounding the data point set is given as an initial shape. The surface shrinks and splits smoothly by repeated erosions with a ball structuring element, whose size increases continuously as the curvature of the surface increases. Every surface point stops moving if it lies in the stopping region. After this surface fitting to the stopping region, the region is narrowed, and the fitting-and-narrowing cycle is repeated until the model is consistent with the object surface on the basis of a topological check. After the above rough estimation, for model refinement, we extract a quadrangular mesh from the resultant surface, and then perform precise fitting to the 3D data by using an energy minimisation.

Creation of complex technical objects (CTO, such as science-intensive engineering objects, which are characterized by the number of elements and connections equal to 106 and more) – a process containing long subprocesses, complex objects, models, and based on available standards and information technology (IT). It is known from practice that CTO models are created with the help of information systems - components of CALS and PLM-solutions. This process have the following shortcomings: models at different stages of creation of CTO are not completely interconnected; CTO modeling takes place using different components of IT CALS and PLM-solutions, which creates constant difficulties in data conversion and leads to partial or complete loss of model data; there is no automated communication with other CTO models.
 A generalized model of complex technical objects is proposed, which based on using the theoretical-multiple approach allows to establish an information connection between the models of the process of creating complex technical objects. The information system of creation of the generalized model of difficult technical objects that allowed automating process of processing and construction of models is developed; to supplement the technology of parallel PLM design and components of information technologies CALS and PLM-solutions for the task of creating models of complex technical objects.

Object shape information is an important parameter in robot grasping tasks. However, it may be difficult to obtain accurate models of novel objects due to incomplete and noisy sensory measurements. In addition, object shape may change due to frequent interaction with the object (cereal boxes, etc). In this paper, we present a probabilistic approach for learning object models based on visual and tactile perception through physical interaction with an object. Our robot explores unknown objects by touching them strategically at parts that are uncertain in terms of shape. The robot starts by using only visual features to form an initial hypothesis about the object shape, then gradually adds tactile measurements to refine the object model. Our experiments involve ten objects of varying shapes and sizes in a real setup. The results show that our method is capable of choosing a small number of touches to construct object models similar to real object shapes and to determine similarities among acquired models.

An algorithm for the robust and fast automatic construction of a 3D model of any real object using images from multiple views is presented. The images are taken from a real object rotating in front of a stationary calibrated CCD TV camera. The object silhouettes extracted from the input images, the related turntable positions, and camera orientation are used to construct the volume model of the real object by applying the method of occluding contours. A keypoint in performing this method is a proper volume representation, characterized by low complexity and suitability for a fast computation of volume models. In the presented approach, each volume model is described by pillar-like volume elements (pillars) ensuring a computational complexity proportional to the size of the real object surface and enabling a fast and simple construction of the volume model. The fast performance is due to the simple projection feasibility of those pillars and the easy-to-perform intersection test for the object silhouette with the projected pillars. Results with real image sequences have confirmed the robustness of the developed algorithm even for the modelling of real objects with highly detailed and complex surfaces and the use of imperfect object silhouettes.

A framework that integrates an object model, research methods (workflows), the capture of experimental data sets and the provenance of those data sets for subject-centric research is presented. The design of the Framework object model draws on and extends pre-existing object models in the public domain. In particular the Framework tracks the state and life cycle of a subject during an experimental method, provides for reusable subjects, primary, derived and recursive data sets of arbitrary content types, and defines a user-friendly and practical scheme for citably identifying information in a distributed environment. The Framework is currently used to manage neuroscience Magnetic Resonance and microscopy imaging data sets in both clinical and basic neuroscience research environments. The Framework facilitates multi-disciplinary and collaborative subject-based research, and extends earlier object models used in the research imaging domain. Whilst the Framework has been explicitly validated for neuroimaging research applications, it has broader application to other fields of subject-centric research.