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.

International audienceIn 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 tracking of 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 vision-based 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

3U3D - Usage, Usability, and Utility of 3D City

Urban environment constitutes a challenging area for pedestrian navigation. However, with the recent increase of pedestrians owning devices (e.g. smartphones), complementary data provided by integrated low cost sensors (camera, Inertial and Magnetic Measurement Unit and GNSS receiver) may be used in a coupling process to accurately estimate the pose (i.e. 3D position and 3D orientation) of a handheld device. Additionally, the actual development and availability of 3D GIS content constitutes a mine of data usable for camera pose estimation. In the context of pedestrian navigation in urban environment, to update a Pedestrian Dead-Reckoning process and to improve the positioning accuracy, we propose to fuse the pose estimated through a vision process thanks to a precisely known 3D model with inertial and magnetic measurements. Experimental data collected in an urban environment, on a long pedestrian path with sparse known models permit to validate the benefit of sensors fusion process. This results in an improved positioning accuracy that enhances the Pedestrian Dead-Reckoning process and enables to display 3D information in Augmented Reality. Performance are presented in terms of positioning accuracy and compared to commonly used solutions.