Called FlexFusion, the algorithm technology processes data from GNSS, inertial management units (IMU) and odometers to provide precise positioning in all conditions. FlexFusion’s design relies on a modelisation of GNSS and IMU (triple-axis accelerometer, triple-axis gyrometer and triple-axis magnetometer) output for synthetic trajectory generation.
The fusion approach exploits Bayesian estimators, e.g. Kalman filters. The first algorithm was setup on modelling signals and its optimization relies on an extensive real-life sample database acquired through field test. This process used CEA-Leti’s HYLOC reference platform, which provides a reference positioning of a few centimeters. More than 100 trajectory samples were collected in urban, suburban, forest and mountain environments with different GNSS outage conditions.
The new positioning technology supports edge AI because the data-fusion algorithm is performed locally to ensure that positioning and navigation information is available locally and is fail-safe even in case of jamming or spoofing of GNSS data.