On Depth Usage for a Lightened Visual SLAM in Small Environments

AbstractHistorically popular, the well established monocular-SLAM is however subject to some limitations. The advent of cheap depth sensors allowed to circumvent some of these. Related methods frequently focus heavily on depth data. However these sensors have their own weaknesses. In some cases it is more appropriate to use both intensity and depth informations equally. We first conduct a few experiments in optimal conditions to determine how to use good quality information in our monocular based SLAM. From this we propose a lightweight SLAM designed for small constrained environments

Science Arts & Métiers (SAM)

International audienceAugmented Reality (AR) technology facilitates interactions with information and understanding of complex situations. Aeronautical Maintenance combines complexity induced by the variety of products and constraints associated to aeronautic sector and the environment of maintenance. AR tools seem well indicated to solve constraints of productivity and quality on the aeronautical maintenance activities by simplifying data interactions for the workers. However, few evaluations of AR have been done in real processes due to the difficulty of integrating the technology without proper tools for deployment and assessing the results. This paper proposes a method to select suitable criteria for AR evaluation in industrial environment and to deploy AR solutions suited to assist maintenance workers. These are used to set up on-field experiments that demonstrate benefits of AR on process and user point of view for different profiles of workers. Further work will consist on using these elements to extend results to AR evaluation on the whole aeronautical maintenance process. A classification of maintenance activities linked to workers specific needs will lead to prediction of the value that augmented reality would bring to each activity

Usability of Augmented Reality in Aeronautic Maintenance, Repair and Overhaul

Augmented Reality (AR) is a strong growing research topic in several areas including industry, training, art and entertainment. AR can help users to achieve very complex tasks by enhancing their vision with useful and well-adapted information. This paper deals with evaluating the usability of AR in aeronautic maintenance training tasks. A case study in the on-site maintenance department was conducted using an augmented reality application, involving operators at several levels of expertise. Obtained results highlighted the full efficacy of AR in the field of aeronautic maintenance

Augmented Reality assistance for R&D assembly in Aeronautics

This paper presents an AR system architecture for assisting complex assembly work by adding visual information superimposed on the physical assembly parts

3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter

The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Square Error (RMSE) was measured by comparing estimated 3D poses and truth data, resulting in a mean error of 0.065 m when walking action was applied

Combining HoloLens and Leap-Motion for Free Hand-Based 3D Interaction in MR Environments

The ability to interact with virtual objects using gestures would allow users to improve their experience in Mixed Reality (MR) environments, especially when they use AR headsets. Today, MR head-mounted displays like the HoloLens integrate hand gesture based interaction allowing users to take actions in MR environments. However, the proposed interactions remain limited. In this paper, we propose to combine a Leap Motion Controller (LMC) with a HoloLens in order to improve gesture interaction with virtual objects. Two main issues are presented: an interactive calibration procedure for the coupled HoloLens-LMC device and an intuitive hand-based interaction approach using LMC data in the HoloLens environment. A set of first experiments was carried out to evaluate the accuracy and the usability of the proposed approach

Free Hand-Based 3D Interaction in Optical See-Through Augmented Reality Using Leap Motion

In augmented reality environments, the natural hand interaction between a virtual object and the user is a major issue to manipulate a rendered object in a convenient way. Microsoft’s HoloLens (Microsoft 2018) is an innovative augmented reality (AR) device that has provided an impressive experience for the user. However, the gesture interactions offered to the user are very limited. HoloLens currently recognizes two core component gestures: Air tap and Bloom. To solve this issue, we propose to integrate a Leap Motion Controller (LMC) within the HoloLens device (Figure 1). We thus used 3D hand and finger tracking provided by the LMC to propose new free hand-based interaction more natural and intuitive. We implemented three fully 3D techniques for selection, translation and rotation manipulation. In this work, we first investigated how to combine the two devices to get them working together in real time, and then we evaluated the proposed 3D hand interactions

Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry

Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. Maintenance activities in aeronautics consist of complex tasks carried out on various high-technology products under severe constraints from the sector and work environment. AR tools appear to be a potential solution to improve interactions between workers and technical data to increase the productivity and the quality of aeronautical maintenance activities. However, assessments of the actual impact of AR on industrial processes are limited due to a lack of methods and tools to assist in the integration and evaluation of AR tools in the field. This paper presents a method for deploying AR tools adapted to maintenance workers and for selecting relevant evaluation criteria of the impact in an industrial context. This method is applied to design an AR tool for the maintenance workshop, to experiment on real use cases, and to observe the impact of AR on productivity and user satisfaction for all worker profiles. Further work aims to generalize the results to the whole maintenance process in the aeronautical industry. The use of the collected data should enable the prediction of the impact of AR for related maintenance activities

Deep Learning for Additive Manufacturing-driven Topology Optimization

This paper investigates the potential of Deep Learning (DL) for data-driven topology optimization (TO). Unlike the rest of the literature that mainly applies DL to TO from a mechanical perspective, we developed an original approach to integrate mechanical and geometrical constraints simultaneously. Our approach takes as input the mechanical constraints (Boundary conditions, loads configuration, volume fraction) alongside the geometrical ones (total number of elements, minimum overhang, maximum length, minimum thickness) and generates a 2D design complying with these constraints. Thus, it combines the best of both mechanical (CAE) and geometrical design worlds. Conversely, geometrical design constraints are complex, not yet formalized, and contradictory between Additive Manufacturing (AM) processes, applications, and materials. Some are even descriptive, lacking a well-defined mathematical description, or are well-defined but proprietary and inaccessible. Hence, despite the synergy between AM and TO, integrating AM constraints into the TO formulation is still a hurdle. Furthermore, even when their integration is possible, TO’s convergence to a solution is compromised. On the other hand, DL has proven robust in capturing geometrical and spatial correlations. Consequently, our approach solves the previously listed setbacks by aligning DL to serve Design for AM (DfAM); there is no need to identify an analytical formula for a geometrical constraint but simply a sufficient number of examples describing it, and convergence is no longer a blockade when the DL model is trained on converged designs. Our approach tailors the design’s geometrical aspects with great flexibility and creativity. It reconciles design and manufacturing and accelerates the design life cycle of a part. Moreover, it can be easily updated to include additional constraints and can be implemented in the future into CAD software as a lighter and faster generative design module

Towards improving the future of manufacturing through digital twin and augmented reality technologies

We are on the cusp of a technological revolution that will fundamentally change our relationships to others and the way we live and work. These changes, in their importance, scope, and complexity, is different than what humanity has known until now. We do not yet know what will happen, but one thing is certain: our response must be comprehensive and it must involve all stakeholders at the global level: the public sector, the private sector, the academic world and civil society. Applications for the industrial sector are already numerous: predictive maintenance, improved decision-making in real time, anticipation of stocks according to the progress of production, etc. So many improvements that optimize the production tools every day a little more, and point to possibilities for the future of Industry 4.0, the crossroads of an interconnected global world. This work comes to contribute as a part of this industrial evolution(Usine 4.0). In this paper we introduce a part of a collaboration between industry and research area in order to develop a DT and AR industrial solution as a part of a predictive maintenance framework. In this context, we elaborate a proof-of-concept that was developed in special industrial application.eRolling2 project Région Hauts-de-Franc