CyborgLOC
Adaptive multi-sensors solution for Indoor/Outdoor geolocation
The CyborgLOC project aims at the pre-industrial production of a multi-sensor adaptive solution for nomadic Indoor Outdoor geolocation.
CyborgLOC relies on:
a) the state of the art achieved by the IFSTTAR laboratory in inertial navigation (between 0.35% and 2% deviation over one kilometer),
b) on Deep Learning methods And Big Data to integrate the real-time recognition of human body movements and subtract them in the trajectory calculations of the barycenter,
c) on the expertise of the miniaturized microsystems of geo-location with energy harvesting of SGME as well as its mastery of sensors,
d) on the first prototypes of SGME for an Indoor / Outdoor Geolocation system based on the work of fusion and scheduling of data and calculations.
The consortium brings together four major complementary domains to solve the challenges of the challenge:
a) Inertial navigation, porting and transformation on the CyborgLOC platform of IFSTTAR's very advanced algorithms,
b) Robotics, integration of algorithms and knowledge of Elter for Scheduling and behavior responsive to the environment and situation (including human body movements),
c) Microsystems, miniaturization and integration of electronic microsystems of SGME (Bageo),
d) With a search for energy saving Up to energy harvesting.
These four major domains finally meet around a common theme: an adaptive geolocation system, focusing on movement and environment recognition based on deep learning algorithms for scheduling.
Contact : Miguel Ortiz - miguel.ortiz@univ-eiffel.fr