Experimental Set-up for Evaluation of Algorithms for Simultaneous Localization and Mapping

The precise positioning of mobile systems is a prerequisite for any autonomous behavior, in an industrial environment as well as for field robotics. The paper describes the set up for an experimental platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. Two approaches are compared. First, a local method based on point cloud matching and integration of inertial measurement units is evaluated. Subsequent matching makes it possible to create a three-dimensional point cloud that can be used as a map in subsequent runs. The second approach is a full SLAM algorithm, based on graph relaxation models, incorporating the full sensor suite of odometry, inertial sensors, and 3D laser scan data

A Concept for Virtual Reality Based Industrial Maintenance Training Preparation

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Quality Assurance and Traceability in Containerized Continuous Delivery Process

International audienceCompanies embrace digitalization to have a shorter time to market. This drive for digitalization also demands that the quality of products and services is high. Containerization is one of the concepts that are core to achieving these objectives in information technology today as it is central to the development and delivery of software products. Therefore, to meet the expectations, the containerization process needs state-of-the-art quality assurance. To achieve this, a quality assurance methodical procedure is being developed with bi-directional traceability. Its usefulness is demonstrated by integrating and successfully applying it into the continuous integration and delivery (CI/CD) pipeline of the Volkswagen Group IT

Democratizing Innovation in the Digital Era: Empowering Innovation Agents for Driving the Change

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Artificial Intelligence helps making Quality Assurance processes leaner

International audienceLean processes focus on doing only necessery things in an efficient way. Artificial intelligence and Machine Learning offer new opportunities to optimizing processes. The presented approach demonstrates an improvement of the test process by using Machine Learning as a support tool for test management. The scope is the semi-automation of the selection of regression tests. The proposed lean testing process uses Machine Learning as a supporting machine, while keeping the human test manager in charge of the adequate test case selection. 1 Introduction Many established long running projects and programs are execute regression tests during the release tests. The regression tests are the part of the release test to ensure that functionality from past releases still works fine in the new release. In many projects, a significant part of these regression tests are not automated and therefore executed manually. Manual tests are expensive and time intensive [1], which is why often only a relevant subset of all possible regression tests are executed in order to safe time and money. Depending on the software process, different approaches can be used to identify the right set of regression tests. The source code file level is a frequent entry point for this identification [2]. Advanced approaches combine different file level methods [3]. To handle black-box tests, methods like [4] or [5] can be used for test case prioritiza-tion. To decide which tests can be skipped, a relevance ranking of the tests in a regression test suite is needed. Based on the relevance a test is in or out of the regression test set for a specific release. This decision is a task of the test manager supported by experts. The task can be time-consuming in case of big (often a 4-to 5-digit number) regression test suites because the selection is specific to each release. Trends are going to continuous prioritization [6], which this work wants to support with the presented ML based approach for black box regression test case prioritization. Any regression test selection is made upon release specific changes. Changes can be new or deleted code based on refactoring or implementation of new features. But also changes on externals systems which are connected by interfaces have to be considere

Startup Engagement as Part of the Technology Strategy Planning -How Rheinmetall Automotive Increases Innovation by Using Corporate Venturing

International audienceIn a fast changing environment that is affected by global megatrends, disruptive technological developments and a growing number of new market players, global tier-one suppliers to the automotive industry have to adapt their technology strategy planning by considering current trends in innovation management to maintain their prevailing leading position. This paper presents Rheinmetall Automotive's approach to adopt modern Open Innovation paradigms with a focus on outside-in innovation through Corporate Venturing. Embedded in their Technological Direction Development Process, Rheinmetall Automotive has developed a strategic model to plan startup engagement to improve the current product development and enhance further diversification. Here we describe the several steps how this company-specific strategic model was designed and what prerequisites have to be taken into account to introduce the model in a corporate context

Covering the Human Perspective in Software Process Improvement

Most of the approaches to improve software process focus on formal process descriptions based on models and standards of best practices. However, the human factor has not been covered, as a result, an important gap arises between processes description and process execution. This gap takes special value in Small and Medium Enterprises (SMEs) because most of them do not have enough resources (time, budget and human) to implement a software process improvement (SPI) without a guarantee of any result due to the investment it represents for them. In order to help SMEs in the implementation of SPI initiatives, this paper presents a set of identified needs that SMEs must face in the implementation of SPI since the human perspective. Moreover, the needs are compared with the results of a local study perform at SMEs of Zacatecas. Besides, the paper includes a proposal focusing on two factors which aims to help them covering the set of needs. Finally, the paper shows the relation of these two factors and the SPI manifesto. © Springer-Verlag Berlin Heidelberg 2014

Integrating Stakeholder and Social Network Analysis into Innovation Project Management

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