Section: Partnerships and Cooperations

National Initiatives

ANR

CLEAR

• Title: Compilation of intermediate Languages into Efficient big dAta Runtimes

• Call: Appel à projets générique 2016 défi ‘Société de l’information et de la communication’ – JCJC

• Duration: October 2016 – September 2020

• Coordinator: Pierre Genevès

• See also: http://tyrex.inria.fr/clear

• Abstract: This project addresses one fundamental challenge of our time: the construction of effective programming models and compilation techniques for the correct and efficient exploitation of big and linked data. We study high-level specifications of pipelines of data transformations and extraction for producing valuable knowledge from rich and heterogeneous data. We investigate how to synthesize code which is correct and optimized for execution on distributed infrastructures.

PERSYVAL-lab LabEx

• Title: Mobile Augmented Reality Applications for Smart Cities

• Call: Persyval Labex (“Laboratoire d’excellence”).

• Duration: 2014 – 2017

• Coordinators: Pierre Genevès and Nabil Layaïda

• Others partners: NeCS team at GIPSA-Lab laboratory.

• Abstract: The goal of this project is to increase the relevance and reliability of augmented reality (AR) applications, through three main objectives:

  1. Finding and developing appropriate representations for describing the physical world (3D maps, indoor buildings, ways...), integrated advanced media types (3D, 3D audio, precisely geo-tagged pictures with lat., long. and orientation, video...)

  2. Integrating the different abstraction levels of these data streams (ranging from sensors data to high level rich content such as 3D maps) and bridging the gap with Open Linked Data (the semantic World). This includes opening the way to query the environment (filtering), and adapt AR browsers to users’ capabilities (e.g. blind people). The objective here is to provide an open and scalable platform for mobile-based AR systems (just like the web represents).

  3. Increasing the reliability and accuracy of localization technologies. Robust and high-accuracy localization technologies play a key role in AR applications. Combined with geographical data, they can also be used to identify user-activity patterns, such as walking, running or being in an elevator. The interpretation of sensor values, coupled with different walking models, allows one to ensure the continuity of the localization, both indoor and outdoor. However, dead reckoning based on Inertial Navigation Systems (INS) or Step-and-Heading Systems (SHS) is subject to cumulative errors due to many factors (sensor drift (accelerometers, gyroscopes, etc.), missed steps, bad estimation of the length of each stride, etc.). One objective is to reduce such errors by merging and mixing these approaches with various external signals such as GPS and Wi-Fi or relying on the analyses of user trajectories with the help of a structured map of the environment. Some filtering methods (Kalman Filter, observer, etc.) will be useful to achieve this task.