Team CyborgLOC

 

Adaptive multi-sensors solution for Indoor/Outdoor geolocation

The CyborgLOC project aims at the pre-industrial production of an adaptive multi-sensor solution for indoor-outdoor nomadic geolocation. CyborgLOC is based on: a) the state of the art reached by the IFSTTAR laboratory in inertial navigation (between 0.35% to 2% of deviation over more than one kilometer), b) Deep Learning and Big Data methods to integrate the real-time recognition of human body movements and environmental conditions, c) the SGME’s expertise on geolocation microsystem with energy harvesting as well as its expertise on various sensors, d) the first SGME’s prototype for an Indoor / Outdoor Geolocation System based on data fusion

The consortium brings together 4 major complementary fields to solve the indoor geolocation challenges: a) Inertial navigation, IFSTTAR’s algorithm integration on the CyborgLOC platform, b) Robotics, integration of Elter’s algorithms and knowledge for the scheduling and responsive behavior towards the environment and the situation (including the body’s movements), c) Microsystems, miniaturization and integration of SGME’s electronic microsystems  (Bageo), with a search for energy saving up to energy harvesting. These four major domains finally meet around a common theme: an adaptive geolocation system, using the movement and environment recognition for scheduling data processing.