Our laboratory works for several years on shape capture (curves, surfaces) in static and moving positions, via inertial sensors - e.g. accelerometers ans magnetometers - able to provide information about their own orientation. In fact, in real conditions, sensors do not exactly provide their orientation, the measure is disturbed with external contributions (own motion acceleration, vibrations, magnetic perturbations). This work consists in analysing these disturbances, proposing preprocessing to clean data to obtain "denoised" tangential information to allow the reconstruction of these curves and surfaces.
First, we study the case of the reconstruction of a metallic pipe: we want to reconstruct a curve with magnetic sensors disturbed (the surface reconstruction will be explored afterwards). This work consists in finding the best methods allowing to extract the needed information from these "noised" signals (data fusion, source separation, model of perturbations, adding a new sensor modality,... are domains to explore). In this goal, a bibliographic study will be done firstly by the Post Doc student, then he will have to implement the different methods found, and test the performances with real signals acquired with our system of shape capture in a disturbed environment.