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In recent years Augmented Reality (AR) has become more and more popular, especially since the availability of mobile devices, such as smartphones or tablets, brought AR into our everyday life. Although the AR community has not yet agreed on a formal definition of AR, some work focused on proposing classifications of existing AR methods or applications. Such applications cover a wide variety of technologies, devices and goals, consequently existing taxonomies rely on multiple classification criteria that try to take into account AR applications diversity. In this paper we review existing taxonomies of augmented reality applications and we propose our own, which is based on (1) the number of degrees of freedom required by the application, as well as on (2) the visualization mode used, (3) the temporal base of the displayed content and (4) the rendering modalities used in the application. Our taxonomy covers location-based services as well as more traditional visionbased AR applications. Although AR is mainly based on the visual sense, other rendering modalities are also covered by the same degree-of-freedom criterion in our classification.

Simultaneous Localization and Mapping is now widely adopted by many applications, and researchers have produced very dense literature on this topic. With the advent of smart devices, embedding cameras, inertial measurement units, visual SLAM (vSLAM), and visual-inertial SLAM (viSLAM) are enabling novel general public applications. In this context, this paper conducts a review of popular SLAM approaches with a focus on vSLAM/viSLAM, both at fundamental and experimental levels. It starts with a structured overview of existing vSLAM and viSLAM designs and continues with a new classification of a dozen main state-of-the-art methods. A chronological survey of viSLAM’s development highlights the historical milestones and presents more recent methods into a classification. Finally, the performance of vSLAM is experimentally assessed for the use case of pedestrian pose estimation with a handheld device in urban environments. The performance of five open-source methods Vins-Mono, ROVIO, ORB-SLAM2, DSO, and LSD-SLAM is compared using the EuRoC MAV dataset and a new visual-inertial dataset corresponding to urban pedestrian navigation. A detailed analysis of the computation results identifies the strengths and weaknesses for each method. Globally, ORB-SLAM2 appears to be the most promising algorithm to address the challenges of urban pedestrian navigation, tested with two datasets.

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International audienceIterative methods are now recognized as powerful tools to solve inverse problems such as tomographic reconstruction. In this paper, the main goal is to present a new reconstruction algorithm made from two components. An iterative algorithm, namely the Conjugate Gradient (CG) method, is used to solve the tomographic problem in the least square (LS) sense for our specific discrete Mojette geometry. The results are compared (with the same geometry) to the corresponding Mojette Filtered Back Projection (FBP) method. In the fist part of the paper, we recall the discrete geometry used to define the projection M and backprojection M* operators. In the second part, the CG algorithm is presented within the context of the Mojette geometry. Noise is then added onto these Mojette projections with respect to the sampling and reconstructions are performed. Finally the Toeplitz block Toeplitz (TBT) character of M*M is demonstrated

Abstract. Camera pose estimation from an image has long been a very active research for Video/GIS registration. Different from traditional methods which are based on direct building feature matching, this paper presents an alternative method which uses an overall Skyline feature to determine the camera's orientation. Through our skyline matching method we find out a way to transform an image/model matching problem into a curve matching problem which significantly reduced the computation complexity and make it a possible solution for real-time applications.

Abstract. Museums are filled with hidden secrets. One of those secrets lies behind historical mock-ups whose signification goes far behind a simple representation of a city. We face the challenge of designing, storing and showing knowledge related to these mock-ups in order to explain their historical value. Over the last few years, several mock-up digitalisation projects have been realised. Two of them, Nantes 1900 and Virtual Leodium, propose innovative approaches that present a lot of similarities. This paper presents a framework to go one step further by analysing their data modelling processes and extracting what could be a generalized approach to build a numerical mock-up and the knowledge database associated. Geometry modelling and knowledge modelling influence each other and are conducted in a parallel process. Our generalized approach describes a global overview of what can be a data modelling process. Our next goal is obviously to apply this global approach on other historical mock-up, but we also think about applying it to other 3D objects that need to embed semantic data, and approaching historically enriched 3D city models.

This article presents a new lossless compression algorithm based on a Finite Radon Transform. The baseline of this work consist in encoding the difference between several similar finite Radon projections. Two differential encoding techniques are needed to efficiently encode the Radon transformed data. An intra-projection encoding technique is first performed, this differential encoding process takes benefit of the redundancy present in such projections. The second step consists in encoding the similarities between Radon projections, this is the inter-projection encoding procedure. The used Finite Radon transform (called Mojette transform), allows to ensure both compression and data redundancy. The main novelty of this work is the use of a secure distributed storage or transmission tool in a lossless compression context.