Traceability applications based on Discovery Services

RFID, in its different forms, but especially following EPCglobal standards, has become a key enabling technology for many applications. An essential component to develop track and trace applications in a complex multi-vendor scenario are the Discovery Services. Although they are already envisaged as part of the EPCglobal network architecture, the functional definition and standardization of the Discovery Service is still at a very early stage. Within the scope of the BRIDGE project, a specification for the interfaces of Discovery Services has been developed, together with a prototype to validate the design and different models to enhance supply-chain control through track and trace applications. © 2008 IEEE.

Modelling information requirements in complex engineering services

Services based around complex engineering equipment and systems provide substantial challenges in both the long-term management of the equipment and the need for guaranteed delivery of the related service. One of the challenges for an organisation providing these services is the management of the information that is required to design, deliver and subsequently assess the success of the service. To assist in this process this paper develops a model for capturing, organising and assessing information requirements for these Complex Engineering Services in which information required to support key decisions in the life cycle of the service is identified. The model – referred to as The 12-Box Model for Service Information Requirements – is embedded in a three-phase procedure for providing an assessment of information requirements of a service contract which also provides insight into the capabilities of available information systems in supporting the contract. An illustrative example examining service information in an aircraft availability contract is used to demonstrate the use of the 12-Box Model and associated assessment procedure.

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.