Object Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings

Object localization, and more specifically object pose estimation, in large industrial spaces such as warehouses and production facilities, is essential for material flow operations. Traditional approaches rely on artificial artifacts installed in the environment or excessively expensive equipment, that is not suitable at scale. A more practical approach is to utilize existing cameras in such spaces in order to address the underlying pose estimation problem and to localize objects of interest. In order to leverage state-of-the-art methods in deep learning for object pose estimation, large amounts of data need to be collected and annotated. In this work, we provide an approach to the annotation of large datasets of monocular images without the need for manual labor. Our approach localizes cameras in space, unifies their location with a motion capture system, and uses a set of linear mappings to project 3D models of objects of interest at their ground truth 6D pose locations. We test our pipeline on a custom dataset collected from a system of eight cameras in an industrial setting that mimics the intended area of operation. Our approach was able to provide consistent quality annotations for our dataset with 26, 482 object instances at a fraction of the time required by human annotators

UAVs for Industries and Supply Chain Management

This work aims at showing that it is feasible and safe to use a swarm of Unmanned Aerial Vehicles (UAVs) indoors alongside humans. UAVs are increasingly being integrated under the Industry 4.0 framework. UAV swarms are primarily deployed outdoors in civil and military applications, but the opportunities for using them in manufacturing and supply chain management are immense. There is extensive research on UAV technology, e.g., localization, control, and computer vision, but less research on the practical application of UAVs in industry. UAV technology could improve data collection and monitoring, enhance decision-making in an Internet of Things framework and automate time-consuming and redundant tasks in the industry. However, there is a gap between the technological developments of UAVs and their integration into the supply chain. Therefore, this work focuses on automating the task of transporting packages utilizing a swarm of small UAVs operating alongside humans. MoCap system, ROS, and unity are used for localization, inter-process communication and visualization. Multiple experiments are performed with the UAVs in wander and swarm mode in a warehouse like environment.Comment: Accpeted at the XXIV INTERNATIONAL CONFERENCE ON "MATERIAL HANDLING, CONSTRUCTIONS AND LOGISTICS

A Grid-based Sensor Floor Platform for Robot Localization using Machine Learning

Wireless Sensor Network (WSN) applications reshape the trend of warehouse monitoring systems allowing them to track and locate massive numbers of logistic entities in real-time. To support the tasks, classic Radio Frequency (RF)-based localization approaches (e.g. triangulation and trilateration) confront challenges due to multi-path fading and signal loss in noisy warehouse environment. In this paper, we investigate machine learning methods using a new grid-based WSN platform called Sensor Floor that can overcome the issues. Sensor Floor consists of 345 nodes installed across the floor of our logistic research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors. Our goal is to localize all logistic entities, for this study we use a mobile robot. We record distributed sensing measurements of Received Signal Strength Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon system as the ground truth. The asynchronous collected data is pre-processed and trained using Random Forest and Convolutional Neural Network (CNN). The CNN model with regularization outperforms the Random Forest in terms of localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can be configured flexibly depending on the scenario in the warehouse. The hardware, software and the CNN architecture of the Sensor Floor are open-source under https://github.com/FLW-TUDO/sensorfloor.Comment: This is a preprint version for IEEE I2MTC 202

Swimming with ShARCS: Comparison of On-sky Sensitivity With Model Predictions for ShaneAO on the Lick Observatory 3-meter Telescope

The Lick Observatory's Shane 3-meter telescope has been upgraded with a new infrared instrument (ShARCS - Shane Adaptive optics infraRed Camera and Spectrograph) and dual-deformable mirror adaptive optics (AO) system (ShaneAO). We present first-light measurements of imaging sensitivity in the Ks band. We compare measured results to predicted signal-to-noise ratio and magnitude limits from modeling the emissivity and throughput of ShaneAO and ShARCS. The model was validated by comparing its results to the Keck telescope adaptive optics system model and then by estimating the sky background and limiting magnitudes for IRCAL, the previous infra-red detector on the Shane telescope, and comparing to measured, published results. We predict that the ShaneAO system will measure lower sky backgrounds and achieve 20\% higher throughput across the $JHK$ bands despite having more optical surfaces than the current system. It will enable imaging of fainter objects (by 1-2 magnitudes) and will be faster to reach a fiducial signal-to-noise ratio by a factor of 10-13. We highlight the improvements in performance over the previous AO system and its camera, IRCAL.Comment: 13 pages, 5 figures, SPIE Astronomical Telescopes + Instrumentation, Montreal 201

The \u27Healthy Parks-Healthy People\u27 Movement in Canada: Progress, Challenges, and an Emerging Knowledge and Action Agenda

In this article, we outline progress and challenges in establishing effective health promotion tied to visitor experiences provided by protected and conserved areas in Canada. Despite an expanding global evidence base, case studies focused on aspects of health and well-being within Canada’s protected and conserved areas remain limited. Data pertaining to motivations, barriers and experiences of visitors are often not collected by governing agencies and, if collected, are not made generally available or reported on. There is an obvious, large gap in research and action focused on the needs and rights of groups facing systemic barriers related to a variety of issues including, but not limited to, access, nature experiences, and needs with respect to health and well-being outcomes. Activation of programmes at the site level continue to grow, and Park Prescription programmes, as well as changes to the Accessible Canada Act, represent significant, positive examples of recent cross-sector policy integration. Evaluations of outcomes associated with HPHP programmes have not yet occurred but will be important to adapting interventions and informing cross-sector capacity building. We conclude by providing an overview of gaps in evidence and practice that, if addressed, can lead to more effective human health promotion vis-à-vis nature contact in protected and conserved areas in Canada

The impact of human-machine interaction on machine design in the Social Networked Industry

Dieser Beitrag beschreibt einen neuartigen Ansatz zum Rapid Physical Prototyping von Maschinenkomponenten in der Intralogistik. Zunächst werden aktuelle Maßgaben und Evaluationsmethoden zur Maschinengestaltung wie Gesetze, technische Normen und Industriestandards hinsichtlich ihrer Implikationen auf die Schnittstelle zum Menschen hin untersucht. Diesem Status Quo werden aus dem Leitgedanken der Social Networked Industry (SNI) bestimmte Anforderungen entgegengestellt und Forschungslücken abgeleitet. Die Anforderungen beziehen sich insbesondere auf die funktionale und ergonomische Gestaltung. Es wird ein Verfahren aufgezeigt, welches ein Motion Capturing System als Referenzwerkzeug nutzt, um eine quantitative Analyse der Wechselwirkung zwischen Maschinendesign und der Perspektive Mensch zu ermöglichen.This contribute describes a new approach for rapid physical prototyping of machine components in material flow systems. First, current stipulations and evaluation methods for machine design such as laws, technical norms and industry standards are examined with regards to their implications for the human factor. This status quo is contrasted with the requirements that result from the central ideas of the Social Networked Industry and research gaps are derived. The requirements take functional and ergonomic aspects into account. A procedure to use Motion Capturing as a reference tool is demonstrated. It enables a quantitative analysis of the interdependency of machine design and the human perspective

Integration of virtual reality and optical motion capturing systems into planning and optimization of material flow systems

Bei der Planung und Optimierung von Materialflusssystemen ist eine hinreichend präzise Erfassung der Leistungsfähigkeit des Faktor Mensch nur bedingt möglich. Die Systeme werden vielfach mit Eingabeparametern gespeist, welche bis zu einem gewissen Grad abgeschätzt werden aber in vielen Fällen nicht ausreichend empirisch abgesichert sind. Die Folge können Über- und Unterdimensionierungen sein. Durch die Kopplung von Motion Capturing mit Virtual Reality können Arbeitsplätze und -prozesse am physischen Modell gestaltet, evaluiert und optimiert werden. Die empirisch gewonnenen Daten bilden die später realisierte Leistungsfähigkeit der Komponenten des Materialflusssystems präziser als bisher ab.In the planning and optimizing of material flow systems, the performance of the human factor is often not gathered with sufficient accuracy. In many cases, the systems are fed with input parameters, which are estimated to some extent but lack empirical verification. The result can be over- and underdimensioning. By coupling motion capture with virtual reality, workplaces and processes can be designed, evaluated and optimized based on a physical model. The empirically obtained data represents the later realized performance of the components of the material flow system more accurately

Cyber-Physical Twin Framework for Generating Human Movement Data in Intralogistics

Menschliche Bewegungen zu erkennen, sie zu deuten und für die Analyse manueller Prozesse relevanten Aktivitäten zuzuordnen sind zentrale Herausforderungen der Human Activity Recognition (HAR). Diesen Herausforderungen geht das Trainieren eines Klassifikators mit Daten voraus. Die Erstellung dieser Trainingsdatensätze, bestehend aus Datenaufnahme, Annotation und Revision von Zeitreihen, bedingt einen immensen Aufwand. Aus diesem Grund werden HAR-Methoden überwiegend an simplen Alltagssituationen getestet. Um HAR-Methoden auch für komplexe Umgebungen wie die Intralogistik entwickeln zu können, ist eine neue Form der Datensatzerstellung notwendig. Dieser Beitrag schlägt ein Framework vor, den Aufwand der Datenaufnahme durch Zuhilfenahme cyber-physischer Zwillinge von Menschen zu reduzieren.Recognizing human movements, interpreting them and assigning relevant activities for the analysis of manual processes are central challenges of Human Activity Recognition (HAR). These challenges are preceded by training a classifier with data. The creation of these training data sets, consisting of data acquisition, annotation and revision of time series, requires immense effort. For this reason, HAR methods are mainly tested on simple everyday situations. A new form of data set creation is necessary to develop HAR methods for complex environments such as intralogistics. This contribution proposes a framework to reduce the effort of data acquisition by using cyber-physical twins

Data mining and fault tolerance in warehousing

This paper surveys the significance of data mining techniques and fault tolerance in future materials flow systems with a focus on planning and decision-making. The fundamental connection between data mining, fault tolerance, and materials flow is illustrated. Contemporary developments in warehousing are assessed to formulate upcoming challenges. In particular, the transition towards distributed systems and the increasing data volume is examined. The significance of taking fault tolerance into account is emphasized. Ultimately, research issues are derived by conflating the previous findings. They comprise a holistic approach towards the integration of data science and fault tolerance techniques into future materials flow systems. Tackling these research issues will help to proactively harmonize the data representation to specific data mining techniques and increase the reliability of such systems

Das Potenzial Deep Learning basierter Computer Vision in der Intralogistik

This work describes three deep learning based computer vision approaches, that hold the potential to increase the degree of automation and the productivity of common warehousing procedures. These approaches will focus on: the re-identification of logistical entities, especially when entering and leaving the warehouse; the multi-view pose estimation of logistical entities to track and to localize them on the shop floor; and the category-agnostic segmentation of items in a bin for robotic grasping.Diese Arbeit beschreibt drei Deep-Learning-basierte Computer-Vision-Ansätze, die das Potenzial haben, den Automatisierungsgrad und die Produktivität gängiger Lagerverfahren zu erhöhen. Diese Ansätze konzentrieren sich auf: die Re-Identifizierung von logistischen Einheiten, insbesondere beim Betreten und Verlassen des Lagers; die Multiview-Positionsschätzung von logistischen Einheiten, um sie in der Fabrik zu verfolgen und zu lokalisieren; und die kategorienunabhängige Segmentierung von Artikeln in einem Behälter für das Greifen durch einen Roboter