Section: Contracts and Grants with Industry
Information fusion for localisation (FIL) — ANR Télécommunications
Participants : François Le Gland, Liyun He.
INRIA contract ALLOC 2856 — January 2008 to December 2010.
This ANR project is coordinated by Thalès Alenia Space. Academic partners are LAAS (laboratoire d'architecture et d'analyse des systèmes), TeSA consortium including ENAC (école nationale de l'aviation civile). Industrial partners are Microtec and Silicom.
The overall objective is to study and demonstrate information fusion algorithms for localisation of pedestrian users in an indoor environment, where GPS solution cannot be used. The sought design combines
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a pedestrian dead–reckoning (PDR) unit, providing noisy estimates of the linear displacement, angular turn, and possibly of the level change through an additional pression sensor,
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range and / or proximity measurements provided by beacons at fixed and known locations, and possibly indirect distance measurements to access points, through a measure of the power signal attenuation,
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constraints provided by an indoor map of the building (map-matching),
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collaborative localisation when two users meet and exchange their respective position estimates.
Besides particle methods, which are proposed as the basic information fusion algorithm for the centralized server–based implementation, simpler algorithms such as the extended Kalman filter (EKF) or the unscented Kalman filter (UKF) have been investigated, to be used for the local PDA–based implementation with a map of a smaller part of the building. Constraints could be taken care of automatically with the help of a Voronoi graph [65] , but this approach implies heavy pre–computations. A more direct approach, taking care of constraints on the fly, using a simple rejection method, has been preferred. Adapting the sample size using KLD–sampling [44] has also been investigated, which could be useful in the case of a poor initial information, or if the user walks in poorly informative area (open zone, absence of beacons). Collaboration between users has been implemented [43] , which allows from a user with a poor localization to benefit from the more accurate localization of another user. In this implementation, the latter user is seen by the former user as a ranging beacon with uncertain position. See [16] , [19] for a description of the overall fusion algorithm and an illustration with simulation results.
